Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

• the present invention is directed at a method of operating a decision support system for evaluating a medical intervention plan for the medical intervention associated with a disorder in a human or animal body, as well as to a decision support system therefor.
• Precision medicine is the future of health care. As a technology-intensive and dependent discipline, medicine will be at the vanguard of this impending change. It will impact medical intervention of individual patients, the way in which patients decide about whether and which medical intervention is desired, but also the way clinical trials are done.
• diagnostic- therapeutic or follow-up question may be performed by identifying a cohort of patients, with suitable characteristics and diagnoses (typically inclusion and exclusion criteria), who are willing to participate.
• suitable characteristics and diagnoses typically inclusion and exclusion criteria
• this is sub- optimal because the benefits or disadvantages for given patient characteristics are not specifically defined.
• this may render the group of potential patients too small and the patients themselves too difficult to identify.
• the term ‘medical intervention’ is to be interpreted as including any kind of medical intervention associated with any kind of disorder, regardless of whether the disorder or disease is diagnosed with a patient or whether it relates to a potential future disorder that may be developed (such as in respect of vaccination for a potential disease, or screening of patients (regardless of, or initiated due to their present state of health) to identify potential health risks or potentially undiscovered disorders).
• the term ‘medical interventions’ may thus for example refer to: screening of disease in asymptomatic patients, vaccination against pathogens or chronic disease, diagnosis of diseases in symptomatic patients, treatment of diseases, recruitment in clinical trials (for example trials approved by an institutional review board (IBB)), or a follow-up after treatment or diagnosis.
• IBB institutional review board
• DSS decision support systems
• a method of operating a decision support system for evaluating a medical intervention plan for the medical intervention associated with a disorder in a human or animal body comprising the steps of: obtaining, by the decision support system, patient data indicative of one or more patient characteristics for an individual patient; obtaining, by the decision support system, medical intervention data indicative of the medical intervention plan for the medical intervention; identifying, based on the medical intervention data, at least one prediction model associated with the medical intervention plan; applying, based on the patient data, at least one modifier model for modifying the at least one prediction model based on the patient characteristics of the individual patient, such as to obtain a modified prediction model; and determining, based on the patient data and using the modified prediction model, a medical intervention outcome in terms of one or more probability values associated with one or more health status events indicative of medical intervention result probabilities.
• the present invention enables to perform an early evaluation of a projected or potential medical intervention plan, prior to the medical intervention plan being carried out, for individual patients.
• the method enables to provide a tailored risk assessment of a certain medical intervention plan, by enabling to modify a prediction model on the basis of individual patient characteristics.
• the method enables the decision support system to apply the risk assessment of the first study to an individual patient, while taking into account specific patient characteristics of that patient.
• the method enables the decision support system to apply the risk assessment of the first study to an individual patient, while taking into account specific patient characteristics of that patient. For example, if it is quantified by a second study that the specific type of tumor with which the individual patient is diagnosed will respond poorly to any type of chemotherapy and that there is a high risk of toxicity and decrease of quality of life, then the risk assessment of the first study can be modified by the decision support system based on the quantification by the second study. The decision support system will then provide an individualized risk assessment for this specific case, enabling the patient to conclude that the side effects of the medical intervention will be more significant in view of the expected diminished benefits.
• NSCLC non-squamous cell lung cancer
• any disorder may be evaluated in this way, even regardless of whether an individual patient really suffers from the disorder.
• any medical intervention may be evaluated in this way, such as screening of diseases in asymptomatic patients, non-compulsory vaccination, diagnosis in symptomatic patients, treatment, recruitment in a clinical trial and follow-up of a disease.
• screening strategy and diagnostic plan e.g. do we need to do a screening with MR for breast in young patients or of pancreas cancer?
• Do we do an invasive biopsy an expensive imaging or a genetic test that could have side effects e.g.
• the decision support system enables to evaluate hypothetical cases as well.
• an individual user wants to assess the risk of side effects of vaccination for COVID 19, in that case the user may provide biometric, genetic data, HLA subtype and other relevant information as patient data to the decision support system, and select the application of a particular COVID 19 vaccine as medical intervention plan.
• the decision support system may access a database in order to obtain a prediction model for the respective vaccine.
• the prediction model in accordance with the invention, can be modified based on specific patient characteristics of the user.
• the impact of this specific patient characteristic state may be quantified to provide a modified prediction model, e.g. on the basis of other studies or an estimation.
• the decision support system will determine, based on the patient data provided by the user and using the modified prediction model, a medical intervention outcome in terms of probability values associated with one or more health status events indicative of medical intervention result probabilities. In this case, the decision support system may show that the impact of the user’s auto immune illness on the side effects of vaccination for COVID19 will be limited.
• the patient data can be clinical and/or demographic (e.g. age, body mass index general status, number of steps done per day), biological (e.g. blood tissue biomarkers), genetic (germ cells somatic mutations or polymorphism, HLA haplotype, somatic mutations of cancer cells), (quantitative) imaging data or data based on sensors (e.g. number of step, use of the phone, speed of speech, type of speech, glycemia, blood biomarkers) or digital questionnaire.
• biological e.g. blood tissue biomarkers
• genetic genetic cells somatic mutations or polymorphism, HLA haplotype, somatic mutations of cancer cells
• quantitative imaging data or data based on sensors e.g. number of step, use of the phone, speed of speech, type of speech, glycemia, blood biomarkers
• digital questionnaire e.g. number of step, use of the phone, speed of speech, type of speech, glycemia, blood biomarkers
• the invention enables to compare two or more different medical intervention plans with each other.
• the term medical intervention plan is to be interpreted broadly, such as to include for example a ‘do nothing’ strategy.
• this will for example enable the user to evaluate the effects of vaccination for C0VID19 in comparison with doing nothing and attracting an infection with the coronavirus SARS-COV-2, in order to support a decision to take the vaccine.
• the method of operating a decision support system in accordance with the present invention enables take away any doubt prior to the medical intervention, in this case vaccination, the advantageous of which, for the individual patient and in the given example to the society, are well understood.
• the method further comprises a step of obtaining preference data indicative of a health status preference or risk profile preference of the patient related to the medical intervention associated with the disorder; and comparing the preference data with the one or more probability values for determining an agreement level therebetween.
• the user may evaluate the medical intervention in relation to certain desired personal health state criteria. For example, suppose a certain individual patient is single, has small children and want to decrease the risk of a life threatening disease, some screening examinations could be indicated. As another example, suppose a certain individual patient wants to be able to rely on certain body functions after medical intervention, e.g. in order to be able to continue practicing his/her profession, the maintenance of that body function may be provided as a health criterion, i.e.
• preference data For example, a photographer suffering from a tumor (e.g. an Optic Nerve Sheath meningioma) behind one of his eyes, may prefer to receive a medical intervention which provides — in addition to curing the disorder - a good probability of maintaining sight in both eyes.
• a piano player suffering from a repetitive strain injury in one of his/her arms will prefer a medical intervention that allows him/her to continue playing.
• the decision support system in accordance with these embodiments may take such preferences into account and specifically allows to evaluate the medical intervention in terms of these preferences, e.g. to maximize the probability that the outcome will match the preferences.
• Providing preferences is not essential and may be dispensed with for other embodiments.
• these preferences may for example be obtained by anamnesis, captured from family members or predicted based on previously trained models or database of similar patients having given in the past their preference (“imitation learning”).
• the “static” preferences before the medical interventions can be taken into account but also the dynamic preferences varying with time.
• the preferences have an impact of the weight of certain models in the prediction of the benefits of the medical intervention but can also play a role in the chosen models (e.g. if there are three toxicity models related to a medical intervention but only one toxicity “x” is relevant for the patient, only the prediction model of toxicity “x” will be taken into account in the baseline DSS).
• the prediction model provides statistics data, wherein the statistics data enable to determine, based on at least one of the patient characteristics, at least one of the probability values associated with at least one of the health status events.
• the prediction models may be based on any study performed to evaluate a medical intervention or the impact of certain health states. Many of such studies quantify the results based on regression models over the cases concerned during the study. These regression models are typically be provided by nomograms.
• a nomogram also called a nomograph, alignment chart, or abaque, is a graphical calculating tool: a two-dimensional diagram designed to allow the approximate graphical computation of a mathematical function. It typically consists of a set of n scales, one for each variable in an equation.
• the at least one prediction model is a regression model providing a predictable dependency between the at least one of the patient characteristics and the at least one of the probability values
• the method comprises a step of: receiving, by the decision support system, a nomogram wherein the nomogram quantitatively describes the dependency between the at least one of the patient characteristics and the at least one of the probability values; and analyzing the nomogram such as to determine, from the nomogram, coefficients of the dependency.
• the nomogram may be analyzed by generating non-real patient characteristics and performing a curve fit over the predicted probability values. This will yield the coefficients which define the prediction model, which can then be stored as prediction model data for use by the decision support system.
• These models alternatively or additionally also be provided by digital interface and be fed with input data manually and/or automatically from the personal patient database, phone, electronic health record, GPS tracking device or public database of weather, pollution, allergens, presence of air particles, risk of infection or a terrorist attack (this list is not exhaustive). This allow to continuously and automatically update the models with the new continuously changing variables.
• the modifier model includes modifier data, the modifier data being indicative of an impact of a particular patient characteristic state on the at least one of the probability values associated with the at least one of the health status events.
• the modifier data may quantify - as described earlier - the impact of the occurrence of a certain biomarker for that patient, or the impact of a specific habit (e.g. smoking).
• the modifier data includes a hazard ratio associated with the particular patient characteristic state, such as a hazard ratio associated with a biomarker (of any type, e.g. biological, genetic, imaging, volatile organic compounds, etc.).
• a hazard ratio provides the ratio of the hazard rates corresponding to the conditions described by two levels of an explanatory variable.
• the hazard ratio would be 2, indicating higher hazard of the occurrence of the side effect from the medical intervention for patients with that biomarker.
• the modifier model includes one or more further prediction models different from the at least one prediction model associated with the at least one medical intervention plan, the further prediction models enabling to modify the at least one prediction model such as by averaging the probability values or correcting for occurrence of a particular patient characteristic state.
• prediction models evaluating a same medical intervention may be combined in this manner to provide a modified prediction model to increase the accuracy or allow to correct for specific cases occurring in one of the cohorts concerned in each study. This can be done by averaging the results or adding the results and performing a new regression over the combined groups.
• the two prediction models may be combined in order to provide a modified prediction model in accordance with these embodiments.
• the patient characteristics include one or more elements of a group comprising: demographic variables, such as gender, age; one or more body parameters, such as body size, weight, body mass index, general status or fat percentage; genetic characteristics, such as gene polymorphism, human leukocyte antigen, haplotype or somatic or germ-cell mutations; co-morbidities; one or more body- or physiological characteristics, such as hair color, skin type, speed of speech, type of speech, number of steps done per day, blood type; biological age; imaging data; data acquired from sensors (e.g.
• medical status data such as medications taken, illness progression status, allergic response data, presence of a biomarker, illness data (comorbidity); medical history data, such as medication history, earlier received medical interventions; behavioral data, such as use of social media and phone, practiced sports, physical exercise frequency, smoking habits; location data (automated through link with a geolocalisation/GPS tracking device or not); climate or weather data, such as temperature, humidity, precipitation, pollution, allergens, air particles; environmental or social data, such as risk of infection, occurrence of an epidemy or pandemic, risk of terrorist attacks.
• medical status data such as medications taken, illness progression status, allergic response data, presence of a biomarker, illness data (comorbidity)
• medical history data such as medication history, earlier received medical interventions
• behavioral data such as use of social media and phone, practiced sports, physical exercise frequency, smoking habits
• location data automated through link with a geolocalisation/GPS tracking device or not
• climate or weather data such as temperature, humidity, precipitation, pollution, allergens,
• the medical intervention data include one or more elements of a group comprising: indication of a medical intervention type, such as medical intervention by surgery, interventional radiology (e.g. radiofrequency ablation, microwave ablation,), drug therapy (such as chemotherapy, immunotherapy, targeted agents, hormonotherapy), irradiation therapy or radiotherapy, tumour- treating fields ((TTFields), also known as alternating electric field therapy), movement therapy; medical intervention specifics, such as medicine data of a particular type of medicine to be applied, a data relating to particular type of surgery to be performed; data relating to enrichment of the microbiome, data relating to treatment with a genetically modified organism (virus or bacteria), data relating to treatment with stem cells, data relating to geroprotectors.
• interventional radiology e.g. radiofrequency ablation, microwave ablation,
• drug therapy such as chemotherapy, immunotherapy, targeted agents, hormonotherapy
• irradiation therapy or radiotherapy irradiation therapy or radiotherapy, tumour- treating fields ((TTFields),
• the health status events include one or more elements of a group comprising: cured; recurrence of the disorder after a predetermined time; decreased or accelerated progression of the disorder; occurrence of post-medical intervention adverse effects, such as a medical intervention related disorder or complication, medical intervention related damage to the body, loss of body function; quality of life, depression, happiness, death.
• the step of determining a medical intervention outcome determining a health state score based on the probability values associated with the one or more health status events.
• the health state score may for example be a number between 0 and 10 - wherein 0 relates to death and 10 relates to completely cured with no side effects.
• the decision support system may also provide a risk assessment in terms of probability values, for example: 36% probability of suffering from side effect A, 23% probability of suffering from side effect B, 83% probability of curing the disorder.
• the method comprises comparing a first medical intervention with a second medical intervention for the medical intervention of the disorder in the human or animal body, wherein the step of obtaining the medical intervention data comprises: obtaining first medical intervention data indicative of the medical intervention plan for the first medical intervention, and obtaining second medical intervention data indicative of the medical intervention plan for the second medical intervention; wherein the step of identifying the at least one prediction model comprises: identifying at least one first prediction model for the first medical intervention and identifying at least one second prediction model for the second medical intervention; wherein the step of applying the at least one modifier model is performed for at least one of the first or second prediction model; and the step of determining the medical intervention outcome comprises determining a first medical intervention outcome based on the first and the second prediction model, as modified by the at least one modifier model, such as to yield a first and a second medical intervention outcome; wherein the step of comparing the first medical intervention with the second medical intervention is performed based on the first and a second medical intervention outcome.
• the method comprises performing multiple times during a time period the method comprises the steps of: identifying the medical intervention model, applying the modifier model and determining the medical intervention outcome; and at least one of the steps of: obtaining patient data, obtaining preference data or obtaining medical intervention data, for enabling to capture changes in the data over time.
• it is advantageous to perform the step of obtaining preference data over the course of time as these preferences may shift dependent on the patients age or phase of life. For example, for a parent having children, the preferences may become different once these children no longer live with the parent. Also, the preferences of a person exercising a certain profession may change after retirement.
• a dynamic method for a dynamic decision support system enables to capture such changes and re-evaluate periodically or upon detecting a change.
• RL Reinforced Learning
• Medical interventions e.g. screening procedures or treatment regimes
• model-free methods or learning methods.
• Medical interventions e.g. screening procedures or treatment regimes
• one DSS before the medical intervention is not sufficient what is needed is a “dynamic DSS”. Therefore the prediction of the model continuously improve based on real world events pre- classified as reward (e.g. the quality of life is excellent, the patient has a good urinary function), a positive value function V, and/or penalties (e.g. a side effects, a relapse of the disease) a negative value function V.
• Those reward and penalties modelling the improvement of the models can be determined by expert knowledge.
• the weight of the penalties and/or reward of the models can be modified by the preference of the patient or, one step further, the relevant penalties (e.g. toxicity “x” not toxicity “y”) and reward (e.g. quality of life over quantity of life) can be chosen by the patients.
• This principle can be applied in two situation: a) Before the medical intervention: When a databases is available a new individualized RL-based models can be created with specific penalties and/or rewards based on the preference of the patient. B) During the medical intervention: In case of long term medical intervention such as a treatment of a specific patient or group of patients, the models can be adapted based on the updated RL-based models taking into account the recent events and the updated preference.
• a patient treated for an operated prostate cancer treatment receives adjuvant hormonotherapy which cause sides effects (fatigue, hot flashes, bone thinning (which can lead to broken bones), loss of muscle). Having some financial responsibilities toward his son completing his academic studies, he decided initially to maximize his probability of cure. Two year later, the side-effects are still present, he has had a hip fracture, his son is financially autonomous and consequently his preferences have changed towards prioritizing his quality of life over quantity of life.
• the individualized RL-based models propose to stop the adjuvant hormonotherapy: this should lead to an improve Quality of life of 60% and a decrease of biochemical free survival of 7%.
• the approach described in “b” can also be used for a group of patients or within an in silico trial by a legal entity such as a department, a pharmaceutical company, a clinical trial organization, an hospital or a government as a way to optimize the treatment based on preferences-based reward and/or penalties.
• the step of obtaining, by the decision support system, patient data comprises: generating, by a controller, patient data for a plurality of individual virtual patients, wherein each individual virtual patient includes one or more patient characteristics associated therewith, the plurality of individual virtual patients thereby forming a virtual cohort.
• This particular class of embodiments allows to perform an in silico clinical trial based on virtually generated patient data or reusing real data of patients treated in the past with validated Al -based models.
• Figure 1 is a schematic illustration of a decision support system in accordance with an embodiment
• Figure 2 is a schematic illustration of a method of operating a decision support system, in accordance with an embodiment
• Figure 3 is a schematic illustration of a method of comparing medical interventions in accordance with an embodiment
• Figure 4 schematically illustrates a challenge encountered with performing a clinical trial amongst a small population of patients
• Figure 5 is a schematic illustration of a method for performing in silico trials using a decision support system, in accordance with an embodiment
• Figure 6 is a schematic illustration of a method of obtaining prediction model data from clinical studies, useable as part of a method in accordance with an embodiment of the present invention
• Figure 7 schematically shows a summary of a Markov model for an exemplary case study
• Figure 8 schematically depicts graphs showing distributions of the patient population over different health states over time
• Figure 9 shows a nomogram that may be analyzed in an embodiment of the invention.
• Figure 10 shows a fitted graph obtained using a regression model for the nomograph of figure 9.
• the term ‘medical intervention’ is to be interpreted as including any kind of medical intervention associated with any kind of disorder, regardless of whether the disorder or disease is diagnosed with a patient or whether it relates to a potential future disorder that may be developed (such as in respect of vaccination for a potential disease, or screening of patients (regardless of, or initiated due to their present state of health) to identify potential health risks or potentially undiscovered disorders).
• the term ‘medical interventions’ may thus for example refer to: screening of disease in asymptomatic patients, vaccination against pathogens, diagnosis of diseases in symptomatic patients, treatment of diseases, recruitment in clinical trials (for example trials approved by an institutional review board (IBB)), or a follow-up after treatment.
• IBB institutional review board
• the fields of screening, vaccination, treatment, clinical trial preparation or recruitment, or follow-up treatment could relate for example to the following questions.
• Screening could for example include the seeking of answers to questions such as: “what is the expected benefit of a control of the glycemia or a screening colonoscopy?” or “what is the probability of a silent cancer?”.
• Vaccination strategy could for example include the seeking of answers to questions such as: “should a patient receive this vaccine?”, “what will be the level of protection?”, or “what are the side effects and the risk to generate an off-target auto-immune response?”.
• Diagnosis could for example include the seeking of answers to questions such as: “which examination are preferred when a lung cancer or a diabetes is suspected in a symptomatic patient?”.
• Treatment could for example include the seeking of answers to questions such as: “should this disease best be treated by medication, surgery or watchful policy?”.
• Inclusion in a clinical trials could for example include the seeking of answers to questions such as: “what are for this particular patient the benefits and the risks of inclusion in a clinical trial?”.
• follow-up treatment of a disease could for example include the seeking of answers to questions such as: “after the treatment of a cancer or a COPD how often should the patent come back and which examination should be done? Should an app for recording metrics by sensors be used, or are questionnaires preferred?’.
• FIG. 1 schematically illustrates a decision support system 1 in accordance with an embodiment of the present invention.
• a decision support server 3 including a controller 5 and an internal or external memory 6 forms a central part of the system 1.
• the decision support server 3 is connectable to a data communication network 8, which may be a wide area network or local area network or else. Through the data communication network 8, the decision support server 3 is communicatively connected to a plurality of data repositories 10.
• the various databases or data repositories are optional, and in principle may also be available in the internal or external memory 6.
• any of the data used in the method of the present invention may also be obtained from or stored in any of the databases 10, through the data communication network 8.
• database 10 it is to be noted that any number or type of database may be applied either locally or remotely from the decision support server 3.
• the decision support server 3 may be provided as input through any type of input means, such as keyboard 7 or directly from diagnostics equipment 9 such as a CT scanner 9. Furthermore, the decision support system 3 may be connected to a hospital management system (not shown) or other system comprising patient health information to retrieve data therefrom.
• user data may be obtained from a patient/user via a mobile telephone 15 connectable through a wireless link 13 at a base station 12 to a data communication network 8.
• the user may provide patient data via a user interface 16 on his mobile phone.
• the user for example may provide input via an application running on his phone 15.
• Such an application may enable the user to encrypt his personal data such as to safely provide it to the decision support system 3, and prevent the data from being compromised.
• the phone 15 may comprise sensors (e.g. a heartbeat sensor, temperature sensor, or other arbitrary sensor) that enables to capture (live) patient data.
• sensors e.g. a heartbeat sensor, temperature sensor, or other arbitrary sensor
• Such data captured via sensors may be captured via the application mentioned above or in another manner, and provided to the decision support system 3.
• the decision support system 1 may further require medical intervention data.
• the medical intervention data defines the specifics of the medical intervention to be evaluated, and for example may identify a type of screening method, diagnostic algorithm, facultative vaccination, medicine and a prescribed dose, manner of admission and frequency, specifics of a clinical trial or a certain follow-up treatment protocol.
• the medical intervention type or medical intervention data may be indicative of a screening procedure (for example total body MRI, CT of the pancreas), or a surgical medical intervention to be performed (for example the removal of a part of the pancreas or the removal of a gall bladder).
• the medical intervention data may be indicative of a specific vaccination with a vaccine having a lower risk of off-target autoimmune response, an immunotherapy medical intervention to be performed for a certain type of cancer.
• any type of medical intervention e.g. screening, vaccination, diagnosis, trial inclusion, treatment, follow-up
• the invention is not limited to the medical intervention of tumors, but may more generally be applied to medical intervention of many kinds of disorders.
• the invention may be applied to support the medical intervention of screening of hypertension with a sensor; a case of covid- 19, or a medical intervention of a repetitive strain injury, a trauma, or a bacterial infection; an inclusion in a IRB approved clinical trial with a app-based trial patient decision aids or a follow-up with a digital health app in which a model is used to decide when the patient should be seen by a doctor and at which interval.
• a medical intervention data may therefore also be indicative of, for example, a type of motion treatment to be performed by physiotherapy .
• the invention is not limited to these types of medical intervention, and any other medical intervention type may be considered.
• the method may be applied to evaluate medical intervention types that are described very detailed, such as screening for breast cancer with Magnetic resonance imaging and genetic test rather than standard mammography such as the admission of a particular drug in a particular admission scheme and dose distribution to cure a certain disease occurring in a particular situation (e.g. as a complication to another disorder).
• medical interventions at a high level, such as comparing the coverage and side effects of two vaccines (based on epitope hotspot, homology of protein vaccine with protein of human proteome, HLA profile of a population); such as comparing radiation treatment versus a surgical intervention for tumors occurring in a certain region of the body, or comparing radiation treatment versus surgical intervention in general.
• the method of the present invention further relies on the application of prediction model data which is indicative of prediction models which enable to predict a certain outcome of the medical intervention.
• a prediction model may be indicative of predicting a probability of survival, a probability of curing the disease, but also a probability of the occurrence of an undesired side effect.
• the prediction model may be indicative of the chance of diminishing or losing a certain body function, for example paralysis of a limp. It is to be understood that these prediction models are typically based on clinical studies that are performed and published in the field. For example extensive clinical studies are to be performed prior to allowing access and registration of medication for specific diseases.
• the prediction model data may typically be obtained from database 10 of decision support system 1.
• the method uses modifier data that enables to modify the prediction model data in order to for example provide a more accurate prediction or to correct for the occurrence or existence of a specific patient characteristic (such as a biomarker, HLA type, proteomic profile and homology with epitopes of a vaccine or a certain patient habit).
• a specific patient characteristic such as a biomarker, HLA type, proteomic profile and homology with epitopes of a vaccine or a certain patient habit.
• These modifier data may also be obtained from clinical studies and case studies such as the prediction model data mentioned hereinbefore.
• the modifier data may also be obtained from a clinical study targeted at studying the specific effect of a certain biomarker or characteristic on the curing of a disease or on the chance of complications. This data may also be obtained from the database 10.
• FIG. 2 schematically illustrates a method in accordance with the present invention.
• the patient characteristics or patient data is obtained for example from a database 10 or from user input, e.g. via the keyboard 7 or the mobile telephone 15 or a different manner of input.
• the patient data may be obtained from the patients file in the hospital management system, his personal health record or his mobile device 15, which comprises or is itself linked to sensors and/or includes an application enabling to input or provide such data.
• Such an application may also capture data from or provided via speech input by the user. This may be supplemented by data provided by a medical practitioner or data obtained from diagnostics equipment such as an imaging system 9.
• step 22 medical intervention data is obtained for the medical intervention to be evaluated. Again this data may be provided by the medical practitioner, or a desired medical intervention to be evaluated may already be registered in the hospital management system in the patient’s file. In embodiments wherein multiple medical interventions are to be compared with each other, step 22 may comprise the obtaining of medical intervention data for each of these medical interventions.
• the decision support system 1 will obtain a prediction model data from database 10.
• the prediction model data will relate to the medical intervention or medical interventions to be evaluated.
• applying the prediction model data for the prediction model obtained for database 10 to the characteristics provided in the medical intervention data and the patient data will provide a general prediction of the outcome of a certain medical intervention which is to some extent already individualized based on the variables available in the prediction model data. For example, if the clinical study underlying the prediction model discriminates between patients of a certain age, then age is a variable and if the patient to be reviewed is of a certain age, say 60 years old, the prediction model may provide the general probability values for a 60-year-old patient.
• step 26 the patient characteristics will be analyzed to identify certain specifics of the patient or his/her disorder that may give rise to a modification of the prediction model obtained from database 10 in step 24.
• the specific patient may suffer from a comorbidity that significantly impacts the probability of a positive outcome of the medical intervention, or which may increase the probability of developing a certain side effect.
• step 26 from the database 10, modifier data is obtained that relates to the impact of the occurrence of this specific patient characteristic on the medical intervention results.
• the modifier data may indicate to which extent the presence of a certain biomarker may give rise to the developing of a specific side effect such as chronic headaches.
• the modifier data obtained in step 26 is used in step 28 to modify the prediction model identified in step 24.
• the modified prediction model obtained in step 28 by modification will then be used in step 30 to determine the individualized predicted outcome of the medical intervention evaluated.
• the evaluation result of step 30 may be provided back to the user via his mobile telephone 15, or for example to the medical practitioner via his computer 32.
• a speech processing unit (which may be a software model) translates the predicted outcome 30 into spoken language.
• the spoken language may for example be presented to the user via a speaker 33. This may be the internal speaker of the telephone 15.
• the inventors have gained the insight that the information is conveyed much more effective via spoken word then via text on screen. Therefore, the outputting of the predicted medical intervention results from step 30 via speaker 33 will be advantageous to the patient.
• Figure 3 provides an example of a comparison of two different medical interventions: medical intervention A and medical intervention B. In step 20, patient characteristics 35 are provided to the decision support system.
• patient characteristics 35 may for example include image data 351, specific tumor data about a tumor to be treated (e.g. the type of tumor), clinical data 353 and tumor biology or genetics 354.
• the patient data 35 may further include specifics of the patient itself, such as gender, age, biometric information, comorbidity, habits, profession, patient preference. With respect to patient preference, it is possible that the patient has indicated that in view of his profession or because of any other reason, a certain preferred outcome is desired. For example, the patient may have indicated that he or she does not want to lose a certain body function because this body function is important for him or her to perform his profession.
• a prediction model 38 will be obtained from the database 10. For example, for medical intervention A prediction model 381 will be identified in the database. For medical intervention B, prediction model 383 will be obtained from database 10. Next, based on the patient data the prediction models 38 will be modified using modifier data. In figure 3, for medical intervention A the modifier data will be a further prediction model 382 which more accurately predicts the outcome of the medical intervention for patients with the give patient characteristics 35. Furthermore, for medical intervention B, because the patient has been associated with the occurrence of a specific biomarker, the modifier data consists of data from a case study 391 to the impact of the biomarker on medical intervention B. As may be appreciated, the modifier data may relate to any number of patient characteristics identified in the patient and is not limited to only one specific characteristic or one biomarker.
• the combined prediction model which is corrected using the modifier data, is used to predict an outcome for each of the medical interventions, medical intervention A 401 and medical intervention B 402.
• the predicted outcome is generally referred to as 40.
• the patient may be presented a review of each of the medical interventions for his specific case. This may allow the patient to choose a preferred medical intervention or may enable the medical practitioner to advise the patient on one of the medical interventions for his case.
• cost information may be added that enable to perform a financial evaluation of each of the medical interventions as well. For example, if medical intervention A would be preferred but the costs of medical intervention A are such that this will severely impact the financial situation of the patient after curing the disease, the patient may prefer that alternative medical intervention B instead.
• the cost comparison will be performed in step 45 and provided as an outcome in step 46. The cost comparison is only optional to the present invention.
• Figure 4 schematically illustrates the performance of a clinical trial of a small group of patients.
• a clinical trial is to be performed for certain patients suffering from a specific comorbidity which does not occur very frequently, the size of the population is too small to be able to perform a clinical trial reliably, and for that reason such a clinical trial cannot be carried out.
• the population of patients 50 contains two patients 501 and 502 that both suffer from the same comorbidity.
• a clinical trial would provide insight in the impact of this comorbidity on the medical intervention by medication 52, however, the size of the group (only two patients) is too small to perform such a clinical trial 55. In this case, no clinical trial will be carried out, and the patients 501 and 502 may be treated anyway without being completely certain of the effects or outcome, or the medical intervention will not be carried out in view of the uncertainty.
• the decision support system 1 of the present invention enables to perform an in silico trial to gain insight in the outcome of the medical intervention for the patients 501 and 502.
• An in silico trial is a clinical trial performed based on simulation only, reusing real data of patient treated in the past or using synthetic data with or without validated models.
• the difficulty with performing an in silico trial in reality is that the reliability of the outcome of such an in silico trial in principle is unknown.
• a cohort 60 of patients 50 may undergo a certain clinical trial for the medical intervention with medication 52.
• the clinical trial 55 will provide a clinical trial result 58.
• the clinical trial 55 is a regular real clinical trial performed amongst the cohort of patients 60.
• the clinical trial results will be published and will be available in the database 10.
• the decision support system 1 may generate an alternative cohort 61 of virtual patients 51.
• the virtual patients 51 are not real patients, but are data elements providing a collection of patient characteristics generated based on statistics. Alternatively here one may also reuse real data of patients treated in the past.
• the virtual cohort 61 will be used to perform an in silico test trial.
• a prediction model 381 will be identified in the database 10.
• modifier data 382 will be obtained from the database 10 and will be used to perform the in silico test trial 65.
• the trial results of the test trial will be available in step 68 as test results.
• the decision support system 1 performs a comparison step 70 between the test results 68 of the in silico test trial 65 and the trial results 58 of the real clinical trial
• step 70 This comparison is performed in step 70. If the results of the clinical trial 55 and the test results 68 are similar and do not deviate too much from each other, this is an indication that the in silico test trial is a success, the calibration and validation succeed and the in silico trial could be reused to test a new hypothesis or to make a prediction for an individual patients. If the test results 68 differ too much from the clinical trial results 58, this is a clear indication that the in silico test trial may have to be further modified. In step 72, the in silico test trial may be further modified by identifying additional modifier data to the prediction models 81. Alternatively, if this cannot be performed successfully it may also be decided to simply cease the in silico trial here.
• step 77 the in silico trial will be performed on a further cohort 73 of patients 74, which include patients 741 and 742 suffering from a same comorbidity.
• the number of participants suffering in the cohort 73 and the number of participants from the comorbidity may be varied based on the trial to be performed.
• the in silico trial 77 will then provide an in silico trial result 78. This can be used as modifier data or as prediction model in the decision support system.
• the above may be applied advantageously in the preparation of clinical trials or to evaluate whether or under what conditions a clinical trial may take place to test a certain hypothesis.
• a sample size needed for a real world clinical trial e.g. how many patients are needed to confirm an difference x between arm 1 and arm 2 for a given alpha and beta
• the invention according to these embodiments allows to test different hypothesis in in silico clinical trials, on real data or synthetic data before a real trial is performed.
• the invention according to these embodiments can be used for “trial Patients Decisions Aids” (tPDA) with models or the trial assumptions used to calculate sample size or the models (form preclinical data, or calculating the probability of cure and toxicity).
• tPDA rial Patients Decisions Aids
• FIG 6 schematically illustrates how prediction model data from a clinical study 80 may be obtained in the decision support system 1.
• a nomogram 80 of the clinical study may be analyzed and digitized.
• the digitized values are analyzed to determine corresponding probabilities.
• a logistics regression model may be applied or a cohort 85 of virtual patient may be generated by the decision support system.
• the coefficients are derived which describe the model, for example by fitting.
• the result of the prediction model is obtained from the nomogram data obtained in step 82. This will produce prediction model data 89 which is provided as data to the database 10.
• the prediction model data 89 may be analyzed by a (linear) regression model to obtain coefficients that describe the prediction model for any arbitrary case.
• the results may be included in the database 10 to replace or be added to the prediction model data 89. A detailed description of such a method will be described further below in this document for a particular example.
• DSS decision support system
• TRIP Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis
• the in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function.
• the DSS is estimated to result in cost savings ( €1005, 95% CI: €159- €1822) and more quality adjusted life years (QALYs; 0 17 years, CI: 0 €0-0 33) than randomized medical intervention selection.
• PCa Prostate cancer
• EBRT external beam radiotherapy
• RP radical prostatectomy
• brachytherapy brachytherapy
• active surveillance is often proposed ( ⁇ 70% and ⁇ 30% respectively), and of the active medical intervention options, RP and EBRT are recommended most often ( ⁇ 50% and ⁇ 45%), according to the Netherlands Cancer Registry.
• DSS clinical decision support system
• the aim of this study was to build such a DSS using predictive models for estimating tumor control and toxicity probabilities for both RP and EBRT for low to intermediate risk localized PCa patients and validate this by comparing to published clinical trials.
• the target population consisted of overall tumor stage T1-T2 PCa patients who were eligible for active medical intervention (i.e. EBRT and RP).
• the DSS was developed by constructing an individual state-transition model to estimate the effects and associated costs of medical intervention with RP vs. EBRT for each patient. Based on patient-specific parameters (e.g. age), and medical intervention type (EBRT or RP), probabilities to develop long-term toxicities including rectal bleeding, urinary incontinence, and impotence, or a combination, are calculated.
• patient-specific parameters e.g. age
• EBRT or RP medical intervention type
• probabilities to develop long-term toxicities including rectal bleeding, urinary incontinence, and impotence, or a combination.
• patients After medical intervention, patients have a risk of progressing to the recurrence state, which is dependent on patient specific parameters (e.g. Gleason score), after which they can develop metastatic disease and subsequently progress to PCa-related death.
• FIG. 7 illustrates a summary of the Markov model 100.
• Ovals 101 represent different health states
• arrows 102 represent transitions between health states.
• Dashed arrow lines 103 are for intelligibility purposes.
• Patients start in the disease-free state, with either all toxicities, ED and UI or UI only, and as time passes, they can recover from toxicity, or progress into the biochemical progression state.
• Death unrelated to cancer can occur from any health state, cancer related death only from the metastatic disease state.
• the DSS then provides a comparison of the tumor control probability (TCP), probability of chronic erectile dysfunction (ED), chronic urinary incontinence (UI), and late rectal bleeding (RB), as well as a comparison of expected costs and quality adjusted life years (QALYs).
• TCP tumor control probability
• ED chronic erectile dysfunction
• UI chronic urinary incontinence
• RB late rectal bleeding
• QALYs expected costs and quality adjusted life years
• the transition probabilities were estimated per individual in order to make this DSS patient specific and ready for precision medicine applications.
• the individual probabilities of progression after medical intervention, and the risk of developing toxicities, were calculated using a selection of regression models or nomograms from the published literature (see table 2 below), adherent to the TRIPOD statement.
• nomograms the coefficients or intercepts were derived (if not reported) by reading the nomogram and using interpolation and fitting.
• Table 2 provides an overview of the literature models used for the state transition probabilities. Rectal bleeding does not typically occur after RP, so the transition was set to zero for this medical intervention type.
• TRIPOD Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis
• EBRT external beam radiotherapy
• RP radical prostatectomy
• n number
• ADT androgen deprivation therapy
• PSA prostate specific antigen
• BED biologically effective dose
• BFFS biochemical failure-free survival
• R2 coefficient of determination
• Gy gray
• BMI body mass index
• C-index concordance statistic
• RFS regression free survival
• UI urinary incontinence
• ASA American Society of Anesthesiologists
• PN pelvic nodes
• V75 volume receiving at least 75 Gy
• RB rectal bleeding *Due to incomplete information, some model coefficients had to be derived from the
• a DSS such as this in combination with the synthetic patient dataset, could function as an in silico clinical trial, a precursor to actual clinical trials, in order to improve study design or explanatory power.
• We demonstrated this by generating a patient dataset with patients aged 75-90 to test the outcomes of different medical interventions for elderly patients, an often underrepresented group in clinical trials.
• the DSS offers the possibility to combine a large quantity of clinical parameters, predict NTCP and TCP and quantify these risks into a single metric for different medical intervention options. This has the potential to improve the decision-making process, along with other factors, such as incorporating patient preferences.
• the development of a DSS fits well into the current trend that strives for personalized medicine, and the results presented in this study confirm the added benefit of such tools.
• the application of the DSS for in silica trials has great potential benefits, not only by improving the design of clinical trials through precursory simulations, it also has the benefit of being able to apply different medical interventions to the same “patient”, which allows for a more objective comparison.
• Another advantage of the DSS is that it is detailed and can further extended with other disease management options such as brachytherapy or active surveillance. It can also be used as the basis for an individualized patient decision aid (iPDA).
• This particular example case study had several limitations. The first one is that this is a model -based study, using models which were trained and validated on different cohorts. The models were selected based on how recently they were published, the number of patients included, and whether or not they used clinical parameters and TRIPOD level. We also attempted to make sure we only selected models trained on patients with similar medical intervention modalities and similar clinical parameters, however not all clinical parameters were reported. Also, the correlation between clinical parameters is not reported, and when generating the synthetic dataset, no correlations were assumed. Different outcomes of the models were validated on different studies, so the DSS as a whole has not been validated on a single patient population.
• this study is exemplary of a detailed, personalized medical intervention DSS that aids in the choice between EBRT and RP for low to intermediate risk PCa patients.
• This DSS could be used for in silico clinical trials when applied to a synthetic dataset, which would be a valuable precursor to clinical trials.
• the results suggest that the full development and clinical application of this DSS would improve the quality of patient care and would be an important step towards personalized and participative medical intervention decisions.
• the objective of this analysis is to create an algorithm able to identify the vaccine that will give the best coverage (at present time and in the future, taking into account the HLA haplotype of the patient and the predicted mutations of the virus) with a sufficiently broad repertoire of T-cell epitopes with the lowest likelihood of side effects in particular anaphylactic shock, fatigue and off-target autoimmune response.
• the SARS-CoV-2 proteome has been sequenced across the most frequent HLA-A, HLA-B and HLA-DR alleles in the human population, using host- infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools. This allowed to generate comprehensive epitope maps.
• This epitope map has then been used as input for a Monte Carlo simulation to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types.
• Critically epitope hotspots that shared significant homology with proteins in the human proteome have been identified to reduce the chance of inducing off-target autoimmune responses. This exercise can be redone for a specific given patient taking into account his HLA type and his proteome (facultative).
• a database of the HLA haplotypes of approximately 15,000 individuals has been built to develop a In Silico “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population.
• Nomograms typically need to processed manually by a user to obtain the information therefrom.
• the nomogram in accordance with these embodiments may be automatically read such as to enable using the prediction model data in the decision support system.
• a nomogram was converted to a spreadsheet.
• the nomogram 120 used for this demonstration wat taken from ⁇ Valdagni 2008 ⁇ , and shown in Figure 9.
• table 6 below was extracted by carefully reading the points on the nomogram 120.
• the probability of late rectal bleeding is described by the following equation: Where: Here is the equivalent uniform dose, determines the steepness of the response, and is the amount of dose given for the probability of late rectal bleeding to be 0.5.
• the prevalence factor is given by: and is assumed to be independent of dose. The prevalence can be calculated using the MAF. To determine the equations for Po and Pi, we must find the corresponding parameters and m o and and ®i. To find DQ we must find the value for EUD where Po is 0.5.
• the present invention has been described in terms of some specific embodiments thereof.
• the method and decision support system of the present application may be applied as a method for:
• this method and decision support system can be used for individualized Patients Decisions Aids (iPDA) with models calculating the probability of cure, toxicity, financial toxicity;
• iPDA Patients Decisions Aids
• this method and decision support system can be used for trials Patients Decisions Aids (tPDA) with models coming form systems biology y, mechanistic modeling, animal or patient data, calculating the probability of cure and toxicity;
• tPDA Patients Decisions Aids

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• 2021
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