Processing device, method, and program

Abstract

This processing device comprises: an acquisition unit that acquires, for a subject of a surgical operation, first information pertaining to each of a plurality of assessment indices of a comprehensive geriatric assessment and second information pertaining to at least one assessment index of a physical function assessment; and a determination unit that determines a risk level of the subject in a perioperative period on the basis of the acquired first information and second information.

Description

Processing device, method, and program

[0001] The present disclosure relates to a processing device, a method, and a program.

[0002] In Japan, where the average lifespan is increasing, the number of elderly patients undergoing surgery is also increasing. Even among the same elderly patients, the perioperative risk level varies depending on the state of their life functions. Examples of perioperative risks include the risk of developing complications, the risk of death, and the like. As a method for comprehensively evaluating the life functions of the elderly, there is the Comprehensive Geriatric Assessment (CGA).

[0003] Recently, for elderly patients, CGA has been performed before surgery to evaluate their preoperative state, and this evaluation result may be used as an indicator of the perioperative risk (for example, Non-Patent Document 1). That is, it is known that there is a correlation between the perioperative risk and the evaluation result of CGA.

[0004] Yamashita, K., Yamasaki, M., Makino, T. et al. “Preoperative Comprehensive Geriatric Assessment Predicts Postoperative Risk in Older Patients with Esophageal Cancer”, Annals of Surgical Oncology 30, 910 - 911 (2023), <https: / / doi.org / 10.1245 / s10434-022-12850-0>

[0005] The inventor has found that by using both the evaluation indicators of CGA and the evaluation indicators of physical function evaluation, the determination accuracy of the perioperative risk level can be improved.

[0006] The purpose of this disclosure is to provide a processing device, method, and program that can improve the accuracy of determining perioperative risk levels. It should be noted that this purpose is only one of several purposes that the various embodiments disclosed herein seek to achieve. Other purposes or problems and novel features will be revealed in the description herein or in the accompanying drawings.

[0007] The processing device according to this disclosure comprises an acquisition unit that acquires first information relating to each of a plurality of evaluation indicators of comprehensive geriatric functional assessment and second information relating to at least one evaluation indicator of physical function assessment for subjects of surgical surgery, and a determination unit that determines the perioperative risk level of the subject based on the acquired first information and second information.

[0008] The method relating to this disclosure is a method performed by a processing device, which includes: obtaining first information regarding each of a plurality of evaluation indicators of comprehensive geriatric functional assessment and second information regarding at least one evaluation indicator of physical function assessment for a subject of surgical treatment; and determining the perioperative risk level of the subject based on the obtained first and second information.

[0009] The program relating to this disclosure causes a processing device to perform the following processes for a subject undergoing surgery: first information regarding each of several evaluation indicators of comprehensive geriatric functional assessment and second information regarding at least one evaluation indicator of physical function assessment; and determining the perioperative risk level of the subject based on the acquired first and second information.

[0010] This disclosure provides a processing device, method, and program that can improve the accuracy of determining perioperative risk levels.

[0011] This is a block diagram showing an example of the processing device of the present disclosure. This is a flowchart showing an example of the processing operation of the processing device of the present disclosure. This is a block diagram showing another example of the processing device of the present disclosure. This is a diagram showing an example of a first input image. This is a diagram showing an example of a first input image. This is a diagram showing an example of a second input image. This is a block diagram showing another example of the processing device of the present disclosure. This is a diagram illustrating a specific example of risk level determination. This is a graph showing the survival rate plotted against the number of days of survival starting from the date of surgery for each subset corresponding to each risk level, using the results of risk level determination based on CGA and physical function assessment for a set of multiple elderly patients. This is a graph showing the survival rate plotted against the number of days of survival starting from the date of surgery for each subset corresponding to each risk level, using the results of risk level determination based on CGA and physical function assessment for a set of multiple elderly patients. This is a graph showing the survival rate plotted against the number of days of survival starting from the date of surgery for each subset corresponding to each risk level, using the results of risk level determination based on CGA for a set of multiple elderly patients. This is a diagram showing an example of the configuration of the processing device.

[0012] The embodiments will be described below with reference to the drawings. In this disclosure, the drawings may be associated with one or more embodiments. Also, each element in the drawings may correspond to one or more embodiments. Furthermore, in the embodiments, the same or equivalent elements are denoted by the same reference numerals, and redundant descriptions are omitted.

[0013] <First Embodiment> <Example of Processing Apparatus Configuration> Figure 1 is a block diagram showing an example of the processing apparatus of the present disclosure. In Figure 1, the processing apparatus 10 has an acquisition unit 11 and a determination unit 12.

[0014] The acquisition unit 11 acquires information on each of the multiple evaluation indicators of the Comprehensive Geriatric Assessment (CGA) (hereinafter sometimes referred to as "first information") and information on at least one evaluation indicator of the physical function assessment (hereinafter sometimes referred to as "second information") for patients undergoing surgical procedures (hereinafter sometimes referred to simply as "patients").

[0015] Here, Comprehensive Geriatric Assessment (CGA) is a method for comprehensively evaluating the functional abilities of older adults. The evaluation indices of CGA may include indicators related to basic activities of daily living (ADL), instrumental activities of daily living, cognitive function, mood, motivation, drive, or nutritional status, or any combination thereof. For example, the multiple evaluation indices of CGA may include any combination of the following: Barthel Index, Lawthon's Instrumental Activities of Daily Living (IADL), Mini Mental Statement Examination (MMSE), Geriatric Depression Scale (GDS), motivation score, and Vitality Index. Furthermore, each evaluation index includes multiple items. The items used in CGA for each evaluation index may be all or some of the items generally defined for that evaluation index. The condition of the patient undergoing surgery is then judged based on each item used in CGA. The subject's condition, as determined for each item used in this CGA, is acquired as the first piece of information mentioned above.

[0016] Furthermore, the evaluation indicators for physical function assessment are indicators for evaluating the decline in the physical function of the subject. For example, the evaluation indicators for physical function assessment may be at least one of the following: a parameter value based on knee extension muscle strength, grip strength, walking speed, or single-leg standing time. The parameter value based on knee extension muscle strength may be, for example, the knee extension muscle strength value itself, or the ratio of knee extension muscle strength to the subject's body weight may be used.

[0017] The determination unit 12 determines the perioperative risk level of the subject based on the first and second information obtained from the acquisition unit 11. This perioperative risk level is an indicator of the type of disease, severity, or probability of onset, length of hospital stay, and the number of days from the date of surgery to death. Because the determination unit 12 uses not only the first information but also both the first and second information to determine the perioperative risk level, it can determine the perioperative risk level more accurately. By accurately determining the perioperative risk level, the quality of perioperative care and postoperative rehabilitation for high-risk patients can be improved, thereby improving the patient's postoperative QOL (Quality of Life) and prognosis.

[0018] <Example of Processing Unit Operation> Figure 2 is a flowchart showing an example of the processing operation of the processing unit of the present disclosure.

[0019] In the processing device 10, the acquisition unit 11 acquires first information and second information about the patient undergoing surgery (step S11). As described above, the first information is information about each of the multiple evaluation indicators of CGA for the patient undergoing surgery. The second information is information about at least one evaluation indicator of physical function assessment for the patient undergoing surgery.

[0020] The determination unit 12 determines the perioperative risk level of the subject based on the first and second information obtained from the acquisition unit 11 (step S12).

[0021] As described above, according to the first embodiment, the acquisition unit 11 in the processing device 10 acquires first information and second information about the subject of surgery. The determination unit 12 determines the perioperative risk level of the subject based on the first information and second information acquired by the acquisition unit 11.

[0022] With the configuration of this processing device 10, the perioperative risk level is determined using not only the first information but also both the first and second information, so the perioperative risk level can be determined more accurately.

[0023] <Second Embodiment> The second embodiment relates to a specific example of a method for acquiring the first information and the second information.

[0024] Figure 3 is a block diagram showing another example of the processing apparatus of the present disclosure. In Figure 3, the processing apparatus 20 has an acquisition unit 21 and a determination unit 22. Figure 3 also shows a terminal 30 together with the processing apparatus 20. The terminal 30 has a display device 31 and an input device 32. The display device 31 is, for example, a liquid crystal panel. The input device 32 is, for example, a touch panel.

[0025] The acquisition unit 21, similar to the acquisition unit 11, acquires first information regarding each of the multiple evaluation indicators of CGA, and second information regarding at least one evaluation indicator of physical function assessment for the subject.

[0026] For example, as shown in Figure 3, the acquisition unit 21 has a display control unit 21A and a reception unit 21B.

[0027] The display control unit 21A causes the display device 31 to display question information and answer information corresponding to each of the multiple evaluation items of each evaluation index of the CGA (hereinafter sometimes referred to as the "first input image"). The display control unit 21A also causes the display device 31 to display an input image for inputting measurement information related to each evaluation index of the physical function evaluation (hereinafter sometimes referred to as the "second input image").

[0028] Figures 4 and 5 show examples of the first input image. Figure 4 shows an example of an image displayed for selecting the evaluation metric to be used for data input from among the multiple evaluation metrics of the Computational Geriatric Assessment (CGA). Figure 4 displays the Barthel Index, Vitality Index, Instrumental Activities of Daily Living (IADL), Geriatric Depression Scale (GDS), and Motivation Score as CGA evaluation metrics. "Input buttons" and "output buttons" corresponding to each evaluation metric are displayed.

[0029] In the image in Figure 4, selecting an "input button" corresponding to a certain evaluation index displays an input image (i.e., the "first image") for entering question information and answers to that question information, corresponding to that evaluation index. Figure 5 shows an example of the first image displayed when the "input button" for the evaluation index "Barthel Index" is selected. In the example in Figure 5, "bathing" and "walking" are displayed as items for the evaluation index "Barthel Index". In the example in Figure 5, question information is displayed asking whether the person is "independent" or "requires partial assistance or is unable" for the item "bathing". By selecting the radio button for "independent" or the radio button for "requires partial assistance or is unable", it is possible to enter the answer information. In addition, in the example in Figure 5, a "comments entry field" is provided where medical professionals can enter their observations in text.

[0030] Once you have finished entering the evaluation metric "Barthel Index," you will be able to select the "Output button" for the evaluation metric "Barthel Index." By selecting this "Output button," you can output the "First Information" for the evaluation metric "Barthel Index." In other words, in the example in Figure 5, the First Information can be output for each evaluation metric. The "Output All button" is used when you want to output the First Information for all evaluation metrics for which you have finished entering the data.

[0031] Figure 6 shows an example of a second input image. Figure 6 depicts an example of a second input image for inputting measurement information related to the evaluation index for physical function assessment. In the example in Figure 6, the evaluation index for physical function assessment is "lower limb muscle strength." This "lower limb muscle strength" corresponds to the knee extension muscle strength mentioned above. Furthermore, in the example in Figure 6, three measurements of "lower limb muscle strength" for the left leg and three measurements of "lower limb muscle strength" for the right leg can be input. In the example in Figure 6, the average value of all input values ​​for "lower limb muscle strength" is calculated and displayed. This average value may be treated as the second information mentioned above.

[0032] The reception unit 21B receives the information input using the first input image and the input device 32 as the first information. The reception unit 21B also receives the information input using the second input image and the input device 32 as the second information.

[0033] The first information includes, for example, score values ​​corresponding to answers to questions for each evaluation item. The second information includes, for example, measured values ​​for each evaluation index of physical function assessment or parameter values ​​based on said measured values.

[0034] The determination unit 22, similar to the determination unit 12, determines the perioperative risk level of the subject based on the first and second information obtained from the acquisition unit 21. The information regarding the determined risk level may be displayed on the display device 31 or another display device (not shown) by the control of another display control unit (not shown) or by the control of the display control unit 21A. That is, the other display control unit (not shown) or the display control unit 21A may form a display control signal that includes the information regarding the determined risk level and transmit the display control signal to the display device 31 or another display device (not shown). <Modification of the second embodiment> In the above description, the acquisition unit 21 was described as acquiring the first and second information by displaying the first and second input images on the display device 31 and inputting the information, but this disclosure is not limited thereto. For example, the acquisition unit 21 may automatically acquire the first and second information from other information systems such as an electronic medical record system or a hospital database.

[0035] <Third Embodiment> The third embodiment relates to a specific example of determining the perioperative risk level.

[0036] Figure 7 is a block diagram showing another example of the processing apparatus of the present disclosure. In Figure 7, the processing apparatus 40 has an acquisition unit 41 and a determination unit 42.

[0037] The acquisition unit 41, like the acquisition units 11 and 21, acquires first information regarding each of the multiple evaluation indicators of CGA, and second information regarding at least one evaluation indicator of physical function assessment for the subject.

[0038] Similar to the determination units 12 and 22, the determination unit 42 determines the perioperative risk level of the subject based on the first and second information obtained from the acquisition unit 21.

[0039] For example, as shown in Figure 7, the determination unit 42 includes a calculation unit 42A, a determination processing unit 42B, and a selection unit 42C.

[0040] The calculation unit 42A calculates the aggregated score for each evaluation index of the CGA based on the first information acquired by the acquisition unit 41. If the second information acquired is measured values ​​for the evaluation index of the physical function evaluation, and the parameter values ​​based on these measured values ​​are used in the judgment by the judgment processing unit 42B described later, the calculation unit 42A may calculate the parameter values ​​based on the measured values ​​for the evaluation index of the physical function evaluation. Here, the measured values ​​for the evaluation index of the physical function evaluation may be the average values ​​described above. Furthermore, if the parameter value based on the measured values ​​is the lower limb muscle strength to body weight ratio described later, the calculation unit 42A may, for example, acquire the body weight value of the subject held in the memory unit (not shown) and use this body weight value to calculate the lower limb muscle strength to body weight ratio.

[0041] The judgment processing unit 42B determines whether the condition (sometimes referred to as the "first condition") is met, which is that the aggregated score values ​​for each evaluation index of the CGA fall within the risk range. The judgment processing unit 42B also determines whether the condition (sometimes referred to as the "second condition") is met, which is that the measured values ​​or parameter values ​​based on said measured values ​​for each evaluation index of the physical function assessment fall within the risk range.

[0042] The selection unit 42C selects the perioperative risk level of the subject from among multiple risk level candidates based on the determination result from the determination processing unit 42B.

[0043] For example, each of a plurality of evaluation indicators of CGA and at least one evaluation indicator of physical function evaluation is classified into a high-risk indicator or a low-risk indicator. The evaluation indicators classified into high-risk indicators may be the Geriatric Depression Scale (GDS), Mini Mental Statement Examination (MMSE), and parameter values based on knee extension muscle strength. Also, the evaluation indicators classified into low-risk indicators may be the Barthel Index, Vitality Index, Instrumental Activities of Daily Living (IADL), and motivation score.

[0044] And when it is determined that the above first condition is not satisfied for all evaluation indicators of CGA and the above second condition is not satisfied for all evaluation indicators of physical function evaluation, the selection unit 42C selects a first risk level candidate.

[0045] Also, when it is determined that the corresponding condition of the above first condition and the second condition is not satisfied for all evaluation indicators classified into high-risk indicators, and the corresponding condition of the above first condition and the second condition is satisfied for at least one evaluation indicator classified into low-risk indicators, the selection unit 42C selects a second risk level candidate.

[0046] Also, when it is determined that the corresponding condition of the above first condition and the second condition is satisfied for some evaluation indicators classified into high-risk indicators, and the corresponding condition of the above first condition and the second condition is not satisfied for all evaluation indicators classified into low-risk indicators, the selection unit 42C selects a third risk level candidate.

[0047] Also, when it is determined that the corresponding condition of the above first condition and the second condition is satisfied for some evaluation indicators classified into high-risk indicators, and the corresponding condition of the above first condition and the second condition is satisfied for at least one evaluation indicator classified into low-risk indicators, the selection unit 42C selects a fourth risk level candidate.

[0048] Further, when it is determined that the corresponding conditions of the above first condition and second condition are satisfied for all evaluation indicators classified as high-risk indicators, the selection unit 42C selects a fifth risk level candidate.

[0049] Here, the first risk level candidate, the second risk level candidate, the third risk level candidate, the fourth risk level candidate, and the fifth risk level candidate increase in risk level in this order.

[0050] Figure 8 illustrates a specific example of risk level determination. Figure 8 shows seven evaluation indicators used for risk level determination and the risk range for each indicator. For example, evaluation indicator (1) is the Barthel Index, and the risk range corresponding to evaluation indicator (1) is 0 to less than 100 points. Evaluation indicator (2) is the Vitality Index, and the risk range corresponding to evaluation indicator (2) is 0 to less than 10 points. Evaluation indicator (3) is IADL (IADL 5 or IADL 8), and the risk range when evaluation indicator (3) is IADL 5 is 0 to less than 5 points, and when evaluation indicator (3) is IADL 8, the risk range is 0 to less than 8 points. Evaluation indicator (4) is the GDS, and the risk range corresponding to evaluation indicator (4) is 5 points or more. Evaluation indicator (5) is the Motivation Test, and the risk range corresponding to evaluation indicator (5) is 16 points or more. Furthermore, evaluation index (6) is MMSE, and the risk range corresponding to evaluation index (6) is 23 points or less. Also, evaluation index (7) is the lower limb muscle strength to body weight ratio, and the risk range corresponding to evaluation index (7) is less than 0.25 if the subject is male, and less than 0.23 if the subject is female. Lower limb muscle strength is, for example, the knee extension muscle strength described above. The lower limb muscle strength to body weight ratio corresponds to the parameter value based on the knee extension muscle strength described above. As stated above, the parameter value based on knee extension muscle strength may be the knee extension muscle strength value itself. That is, the knee extension muscle strength value itself may be used as evaluation index (7). When the knee extension muscle strength value itself is used as evaluation index (7), for example, the risk range corresponding to evaluation index (7) is less than 19.1 kilograms-weight if the subject is male, and less than 16.2 kilograms-weight if the subject is female.

[0051] Furthermore, if the corresponding values ​​for each of the evaluation indicators (1)-(7) are not included in the risk range, the selection unit 42C selects a risk level of "low". Also, if the corresponding values ​​for all of the evaluation indicators (4), (6), and (7) are not included in the risk range, and the corresponding value for at least one of the evaluation indicators other than evaluation indicators (4), (6), and (7) (i.e., evaluation indicators (1)-(3), and (5)) is included in the risk range, the selection unit 42C selects a risk level of "low to medium". Also, if the corresponding values ​​for some of the evaluation indicators (4), (6), and (7) are included in the risk range, and the corresponding values ​​for all of the evaluation indicators other than evaluation indicators (4), (6), and (7) (i.e., evaluation indicators (1)-(3), and (5)) are not included in the risk range, the selection unit 42C selects a risk level of "medium". Furthermore, if the corresponding values ​​for some of the evaluation indicators (4), (6), and (7) are included in the risk range, and the corresponding values ​​for at least one of the evaluation indicators other than evaluation indicators (4), (6), and (7) (i.e., evaluation indicators (1)-(3), and (5)) are included in the risk range, the selection unit 42C selects a risk level of "medium to high". Furthermore, if the corresponding values ​​for all of the evaluation indicators (4), (6), and (7) are included in the risk range, the selection unit 42C selects a risk level of "high".

[0052] Figures 9 and 10 are graphs that plot survival rates against the number of days of survival from the date of surgery for subsets corresponding to each risk level, using the results of risk level determination based on CGA and physical function assessment for a set of multiple elderly patients. In particular, Figure 10 shows graphs for subsets corresponding to high risk levels (referred to as the "high-risk group" in the figure) and subsets corresponding to low risk levels (referred to as the "low-risk group" in the figure), using the results of risk level determination based on CGA and physical function assessment.

[0053] Figure 11 is a graph showing the survival rate against the number of days of survival starting from the date of surgery for each subset corresponding to a risk level, using the results of risk level determination based on CGA for a set of multiple elderly patients. In other words, Figure 11 shows a comparative example. In particular, Figure 11 shows graphs for the subset corresponding to the high-risk level (referred to as the "high-risk group" in the figure) and the subset corresponding to the low-risk level (referred to as the "low-risk group" in the figure), using the results of risk level determination based on CGA.

[0054] As shown in Figure 10, the hazard ratio between the high-risk and low-risk groups based on CGA and physical function assessment is "1.813". As shown in Figure 11, the hazard ratio between the high-risk and low-risk groups based on CGA is "1.522". The further the hazard ratio value is from 1, the greater the difference between the high-risk and low-risk groups. Therefore, it can be seen that Figure 10 is able to determine the risk level more accurately than Figure 11.

[0055] <Other Embodiments> Figure 12 shows an example of the configuration of a processing unit. In Figure 12, the processing unit 100 includes a processor 101, a memory 102, and a communication interface 103. The processor 101 may be, for example, a microprocessor, an MPU (Micro Processing Unit), or a CPU (Central Processing Unit). The processor 101 may include multiple processors. The memory 102 is composed of a combination of volatile memory and non-volatile memory. The memory 102 may include storage located away from the processor 101. In this case, the processor 101 may access the memory 102 via an I (Input) / O (Output) interface, which is not shown. The communication interface 103 communicates with other devices, including the terminal 30 described above.

[0056] The processing units 10, 20, and 40 of the first to third embodiments may each have the configuration shown in Figure 12. The acquisition units 11, 21, and 41 and the determination units 12, 22, and 42 of the processing units 10, 20, and 40 of the first to third embodiments may be realized by the processor 101 reading and executing a program stored in the memory 102. In other words, the processing units 10, 20, and 40 of the first to third embodiments can be realized by software. The program can be stored using various types of non-transitory computer-readable media and supplied to the processing units 10, 20, and 40. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives) and magneto-optical recording media (e.g., magneto-optical disks). Furthermore, examples of non-transitory computer-readable media include CD-ROMs (Read Only Memory), CD-Rs, and CD-R / Ws. Furthermore, examples of non-transitory computer-readable media include semiconductor memory. Semiconductor memory includes, for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, and RAM (Random Access Memory). Programs may also be supplied to the processing units 10, 20, and 40 by various types of transient computer-readable medium. Examples of transient computer-readable medium include electrical signals, optical signals, and electromagnetic waves. The transient computer-readable medium can supply programs to the processing units 10, 20, and 40 via wired communication channels such as electric wires and optical fibers, or via wireless communication channels.

[0057] Alternatively, the acquisition units 11, 21, 41 and the determination units 12, 22, 42 of the processing units 10, 20, 40 of the first to third embodiments may each be implemented with dedicated hardware. Furthermore, some or all of the components of each device may be implemented by general-purpose or dedicated circuits, processors, etc., or combinations thereof. These may be configured by a single chip or by multiple chips connected via a bus. Some or all of the components of each device may be implemented by a combination of the above-mentioned circuits, etc., and programs. Furthermore, a CPU (Central Processing Unit), GPU (Graphics Processing Unit), FPGA (field-programmable gate array), quantum processor (quantum computer control chip), etc., can be used as the processor.

[0058] Furthermore, if some or all of the components of the processing devices 10, 20, and 40 of the first to third embodiments are implemented by multiple information processing devices or circuits, these multiple information processing devices or circuits may be centrally located or distributed. For example, the information processing devices or circuits may be implemented in a form in which each is connected via a communication network, such as a client-server system or a cloud computing system. Also, the functions of the processing devices 10, 20, and 40 of the first to third embodiments may be provided in SaaS (Software as a Service) format.

[0059] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited thereto. Various modifications to the structure and details of the present disclosure can be understood by those skilled in the art. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0060] Each drawing is merely illustrative to illustrate one or more embodiments. Each drawing may be associated with one or more other embodiments, rather than being associated with only one specific embodiment. As those skilled in the art will understand, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings, for example, to create embodiments not explicitly shown or described. Not all features or steps shown in any one drawing to illustrate an exemplary embodiment are necessarily required, and some features or steps may be omitted. The order of steps described in any of the drawings may be changed as appropriate.

[0061] This application claims priority based on Japanese Patent Application No. 2024-211720, filed on 4 December 2024, and incorporates all of its disclosures herein.

[0062] 10 Processing unit 11 Acquisition unit 12 Determination unit 20 Processing unit 21 Acquisition unit 21A Display control unit 21B Reception unit 22 Determination unit 30 Terminal 31 Display device 32 Input device 40 Processing unit 41 Acquisition unit 42 Determination unit 42A Calculation unit 42B Determination processing unit 42C Selection unit

Claims

1. A processing device comprising: an acquisition unit that acquires first information regarding each of multiple evaluation indicators of comprehensive geriatric functional assessment and second information regarding at least one evaluation indicator of physical function assessment for a subject to surgical treatment; and a determination unit that determines the perioperative risk level of the subject based on the acquired first and second information.

2. The processing apparatus according to claim 1, comprising: an acquisition unit which displays a first input image on a display device for inputting question information and answer information to the question information corresponding to each of the plurality of evaluation items of the comprehensive functional assessment for the elderly, and a second input image on the display device for inputting measurement information relating to each evaluation indicator of the physical function assessment; and a reception unit which receives information input using the first input image as first information and receives information input using the second input image as second information.

3. The processing apparatus according to claim 1 or 2, comprising: a calculation unit that calculates aggregated scores for each evaluation index of the comprehensive functional assessment of the elderly based on the acquired first information; a determination processing unit that determines whether or not a first condition is met, that the aggregated scores for each evaluation index of the comprehensive functional assessment of the elderly are included in the risk range, and also determines whether or not a second condition is met, that the measured value or parameter value based on the measured value for each evaluation index of the physical function assessment is included in the risk range; and a selection unit that selects a perioperative risk level for the subject from among a plurality of risk level candidates based on the determination result by the determination processing unit.

4. Each of the multiple evaluation indicators of the comprehensive functional assessment for the elderly and at least one evaluation indicator of the physical function assessment is classified as either a high-risk indicator or a low-risk indicator. The selection unit selects a first risk level candidate if it determines that the first condition is not met for all evaluation indicators of the comprehensive functional assessment for the elderly and the second condition is not met for all evaluation indicators of the physical function assessment. The selection unit selects a second risk level candidate if it determines that the corresponding conditions of the first and second conditions are not met for all evaluation indicators classified as high-risk indicators and the corresponding conditions of the first and second conditions are met for at least one evaluation indicator classified as a low-risk indicator. The selection unit selects a third risk level candidate if it determines that the corresponding conditions of the first and second conditions are met for some of the evaluation indicators classified as high-risk indicators and the corresponding conditions of the first and second conditions are not met for all evaluation indicators classified as low-risk indicators. The apparatus according to claim 3, wherein if it is determined that the corresponding conditions of the first and second conditions are met for some of the evaluation indicators classified as high-risk indicators and the corresponding conditions of the first and second conditions are met for at least one evaluation indicator classified as low-risk indicators, a fourth risk level candidate is selected; if it is determined that the corresponding conditions of the first and second conditions are met for all of the evaluation indicators classified as high-risk indicators, a fifth risk level candidate is selected, and the first, second, third, fourth, and fifth risk level candidates have increasing risk levels in this order.

5. The processing apparatus according to claim 4, wherein the evaluation indicators classified as high-risk indicators are the Geriatric Depression Scale (GDS), Mini Mental Statement Examination (MMSE), and parameter values ​​based on knee extension muscle strength, and the evaluation indicators classified as low-risk indicators are the Barthel Index, Vitality Index, Instrumental Activities of Daily Living (IADL), and motivation score.

6. The apparatus according to claim 1 or 2, wherein at least one evaluation index for the physical function evaluation includes at least one of a parameter value based on knee extension muscle strength, grip strength, walking speed, or single-leg standing time.

7. The processing apparatus according to claim 1 or 2, wherein the multiple evaluation indices for the comprehensive functional assessment of the elderly include any combination of the following: Barthel Index, Instrumental Activities of Daily Living (IADL) on Lawthon's scale, Mini Mental Statement Examination (MMSE), Geriatric Depression Scale (GDS), motivation score, and Vitality Index.

8. A method performed by a processing device, comprising: obtaining first information relating to each of a plurality of evaluation indices of a comprehensive geriatric functional assessment and second information relating to at least one evaluation indice of a physical function assessment for a subject of surgical treatment; and determining the perioperative risk level of the subject based on the obtained first and second information.

9. A program that causes a processing device to perform the following steps for a patient undergoing surgery: first information regarding each of several evaluation indicators of the comprehensive geriatric functional assessment and second information regarding at least one evaluation indicator of the physical function assessment; and determining the perioperative risk level of the patient based on the first and second information obtained.

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