Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06534645
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-29
Brief Title:
STOP-stroke STroke Outcome Prediction in the Acute Treatment Setting
Sponsor:
None
Organization:
None
Study Overview
Official Title:
STOP-stroke STroke Outcome Prediction in the Acute Treatment Setting - a Prospective Single-center Observational Study
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
STOP-STroke
Brief Summary:
The STOP-stroke project aims at improving prediction of outcome early after stroke In order to achieve this we need to understand reasons important variables for prediction in a real clinical prognostication process
We aim to
1 Test the predictive performance of stroke neurologists for outcome prediction NIHSS at 24 hours and 3 months and mRS at 3 months after stroke onset prospectively and in a real clinical setting and to explore the most important baseline variables in their prognostication process
2 Test the prediction performance of our DL models when being provided with structured clinical andor imaging information from the same patients as the neurologists and to discover most relevant features of the input data
3 Use the information gained from our experiments for improving our DL algorithm This will include an error analysis on the missclassifications of models and neurologists to understand the pitfalls of both approaches We anticipate to develop a robust reliable and clinically feasible application ready for testing in a prospective observational trial
We aim to
1 Test the predictive performance of stroke neurologists for outcome prediction NIHSS at 24 hours and 3 months and mRS at 3 months after stroke onset prospectively and in a real clinical setting and to explore the most important baseline variables in their prognostication process
2 Test the prediction performance of our DL models when being provided with structured clinical andor imaging information from the same patients as the neurologists and to discover most relevant features of the input data
3 Use the information gained from our experiments for improving our DL algorithm This will include an error analysis on the missclassifications of models and neurologists to understand the pitfalls of both approaches We anticipate to develop a robust reliable and clinically feasible application ready for testing in a prospective observational trial
Detailed Description:
To avoid irreversible brain damage in acute stroke neurologists must make treatment decisions under immense time-pressure In current clinical practice neurologists decide using visual inspection of brain scans and established clinical parameters Despite the large quantity of data statistical or machine learning models have not yet reached clinical practice to guide decision-making This is in large part because doctors cannot assess the trustworthiness of these models and because current models cannot handle multimodal input data Since the field of acute stroke treatments is constantly evolving there is an urgent need to improve our understanding of the factors determining stroke patient outcome and response to treatment We are convinced that the project will provide new insights into stroke outcome prediction and help to integrate data from machine learning algorithms into clinical routine
Neurologists prediction of stroke outcome When a patient arrives at the stroke unit the treating team continuously integrates information as to what the best therapeutic options or risk of complications may be To predict patient outcomes neurologists rely on structured tabular data such as age sex and blood pressure and unstructured data such as medical image data Current prediction of therapeutic success in stroke patients outside the therapeutic time window is often based on brain images Joundi et al Neurology 97S68-S78 Diffusion and perfusion weighted imaging DWI PWI are used to identify the infarct core and hypoperfused region resulting in an estimate of the tissue at risk referred to as mismatchHeiss WD et al Int J Stroke 14351-8 Large mismatch and small infarct core are considered surrogate markers for treatment success which is why clinical trials have often pre-selected such patients Albers GW et al N Engl J Med 378708-18 However recent studies question the validity of such a pre-selection For instance novel pooled data analyses indicate that patients with large infarct core benefit from endovascular treatment as well Campbell BCW el a Lancet Neurol 1846-55 Moreover studies with the pre-selected patients do not allow drawing any conclusions about the effect of treatment in patients with a lack of mismatch or large cores It becomes increasingly clear that patients with a large core or lack of mismatch could benefit from endovascular thrombectomy treatment as well Karamchandani RR et al J Stroke Cerebrovasc Dis 31106548 We expect that a refined modelling approach and the integration of imaging and clinical parameters will yield better prognostic factors and more reliable outcome predictions
Computer aided prediction of stroke outcome For computer aided outcome prediction in stroke patients scores were developed based on a few preselected tabular features such as age and sex and an underlying logistic regression model While these methods slightly outperform the score-based methods they lack interpretability Therefore currently there are no computer aided outcome prediction tools used in clinical routine
Previous preparatory work In a previously conducted recent project weanalyzed imaging features from DWI and perfusion imaging data such as TTP CBFCBV and TMAX in a group of stroke patients with LVO all treated with MT at the InselSpital Berne We tested if the generally accepted mismatch concept derived from routine clinical software indeed enhances outcome prediction for individual patients We demonstrated that neither the core nor the mismatch volume significantly improved prediction of outcome when used in addition to clinical parameters Hamann J et al Eur J Neurol 281234-43 To implement a stroke outcome prediction model that is trustworthy for clinicians we developed deep learning DL based models that can not only integrate structured and unstructured data but also yield interpretable parameter estimates for clinical parameters like odds ratios httpswwwsciencedirectcomsciencearticleabspiiS003132032100443X We applied them to data from ischemic stroke patients treated at the University Hospital Berne httpsarxivorgabs220613302 and investigated if our models can compete with experienced neurologists by performing a blinded prediction challenge where five neurologists and our trained DL model were provided with the same structured clinical data of stroke patients In this experiment our model tended to show a better performance particularly when image data were provided Herzog L et al Stroke 2023 Jul5471761-1769
Accordingly the present STOP-stroke study aims to improve outcome prediction early after stroke by assessing both the clinicians outcome estimation as well as our trained DL model Thereby we try to implement a new prognostication tool which can be helpful in supporting treatment decision and individualized patient care
Primary and secondary endpoints in STOP-stroke
The primary endpoint of the the study will be the actual stroke related disability assessed by the mRS at 3 months as well as the NIHSS stroke severity after 24 hours and 3 months after acute ischemic stroke AIS Baseline factors that may have an influence on the primary endpoint are age sex vascular risk factors treatment times independence before stroke etc Secondary endpoints are the predicted mRS scores of the treating neurologists on the stroke unit one side and the predicted mRS by the DL model on the other
Project design
This is a single-center project conducted in the Department of Neurology at the USZ The study design will be exploratory
Recruitment screening and informed consent procedure
The location of recruitment will be the Department of Neurology at the USZ We plan to perform a single-center study All patients referred to the stroke unit of the USZ with suspicion of AIS from 7 am until 5 pm will be screened by the study team These patients will be recruited according to a consecutive ongoing recruitment through the study team in daily clinical practice The screening process will take place as described in the following section Patients with suspicion of AIS arriving at the stroke unit of the USZ will be screened if there is no refusal of data use already documented prior objected GC If yes these patients will not be considered to take part in the study If not an independent physician will be asked to consent for the patient in order not to delay the acute treatment The independent physician which can be any physician not part of the study team will be informed that their participation is voluntary and can be withdrawn at any time Patients will be asked for their consent as soon as they are able to consent in the course of the hospitalisation or at least until the routine 3-month follow up visit If until then the patient is still not able to consent we will ask the patients next of kin If the patient has no next of kin there is no contact to the next of kin or the patient is lost to follow-up or the patient has died data will be used if no objection against use of data for research is documented If patients andor next of kin are not able to appear in person for the routine visit we will send them a short letter of request of study participation together with the informed consent via mail Within the letter of request we will explain the informed consent procedure and ask patients either to send back the signed informed consent in case of study participation approval or to give a written statement of denyal of study participation We will inform patients within the letter of request that if there is no reply within four weeks we will use the obtained de-identified data if no prior objection against use of data for research is documented the document of the letter of request can be found as a separate attachment If patients reject to take part in the study the collected coded data will not be used for further data analysis
We developed an informed consent for patients next of kin and the independent physician specifically for the purpose of this study which is attached among the study documents submitted
Study procedures
The study is planned to start immediately after approval by the ethics committee and will continue for an overall project duration of approximately three years We plan a recruitment period of approximately one to two years The project duration for each patient will be three months as we assess the primary outcome parameter mRS three months after AIS As the study consists of a collection of personal health data of patients referred with suspicion of AIS to the USZ These clinical and imaging data will be de-identified and stored on a password secured USZ server We will not collect any biological patient material or samples blood or tissue
Clinical and imaging variables
We plan to collect routine laboratory and imaging data routine clinical scores of stroke severity and functional disability and other clinical routine information The treating attending physician board-certified neurologist on the stroke unit at the USZ will be asked on the time points N1 and N2 and the treating attending physician during the hospitalisation period on N3 to complete a questionnaire see attached at the end of this manuscript accessible only via personal login to a specifically for this project developed website planned with the secure web application REDCap The clinical and imaging parameters will be obtained from the treating physician on the stroke unit or the neurological ward of the USZ or can be extracted from the electronic patient chart KISIM We do not plan to extract data from the Swiss Stroke Registry SSR as the data from the acute setting will not be documented here at that time point The imaging data will be assessed at N2 from the clinically indicated first brain imaging
Additionally we will assess the following parameters by the treating physician on the stroke unit based on the medical history and clinical examination which we determined to be essential for the estimation of outcome frailty multimorbidity uncontrolled cancer pre-existing cognitive decline polypharmacy 4 medications institutional housing weak or no social support family or close friends and an uncategorized slot for up to three other variables not previously listed These parameters will be assessed categorically yesno option from the study team members upon questioning the treating attending physician or via assessment of the electronic patient chart The treating physician will assess the parameters upon clinical examination andor questioning the patient or next of kin if available in the acute setting A list of these parameters is attached at the end of this manuscript The web application REDCap will be designed by the Clinical Trial Center for the purpose of the project All treating attending physicians will receive a personalized login in order to complete the questionnaires for the different study time points directly in REDCap We plan a time-lock for the data entry to fix the data and secure the input of the data within reasonable time Access to the data in REDCap will have only the treating attending physician as well as the study team members The application is password secured for the purpose secure storage of study data
Outcome variables
NIHSS The National Institutes of Health Stroke Scale is a 42-point neurological exam used to quantify the severity of an acute stroke The NIHSS does assess the level of consciousness eye move-ments visual fields facial muscle function extremity strength sensory function coordination language speech and neglect The score will be collected as a standard procedure in AIS patients on admission to hospital at stroke onset and will represent one of the clinical parameters upon which the outcome prediction has to be made The NIHSS will be also a primary outcome parameter at 24 hours and 3 months after stroke onset which has to be predicted by the physicians on N1-3 and will be prospectively asked from the treating physicians on the stroke unit who are performing the score as part of the examination on admission routinely on N1 and asked from the treating neurologists on the ward on N3 which is part of the examination of discharge as well as at the routine 3-months control at the outpatient clinic of the USZ The study team will assess the score via the patient chart or directly from the treating physician of the outpatient clinic of the department of Neurology at the USZ If the patient is not able to present in person at the 3-month control the study team can contact the next of kin general practicioner treating physician or nurse in the rehabilitation center or nursing home in order to assess the score
mRS The modified Rankin Scale is a widely used scale of measuring the degree of disability of people who have suffered a stroke The mRS is used in clinical practice as well as in stroke studies to describe the clinical outcome after stroke The mRS runs from 0 to 6 with 0 being a patient without any disability and 6 indicating for death of the patient The score will be collected as a standard procedure in AIS patients on admission to hospital at stroke onset including the score before stroke onset as well as after three months as an outcome parameter assessed by physicians of the Department of Neurology The score will be prospectively asked on N1-3 as it also represents a primary outcome parameter as well as at the routine 3-months control at the outpatient clinic of the USZ The study team will assess the score at 3 months via the patient chart or from the treating physician of the outpatient clinic of the department of Neurology at the USZ at that time point If the patient is not able to present in person at the 3-month control the study team can contact the next of kin general practicioner treating physician or nurse in the rehabilitation center or nursing home in order to assess the score
Imaging data
We plan to store the imaging data CT and MR images of the brain including CT- and MR-angiography as well as perfusion images of the brain collected on admission to USZ or the hospital of the initial hospitalization as part of the clinically indicated first neuroimaging performed after admission to hospital with suspicion of acute ischemic stroke The imaging data will be taken from the imaging software system used at USZ The de-identification process will be performed according to USZ internal standards A password protected master file will be stored by the data manager on USZ servers which are secured according to USZ guidelines Images will be stored in a de-identified form in a project specific area with restricted access Access will be granted to all project members according to the staff list As data will be archived in a structured way and stored in a coded form imaging data can be made available on reasonable request for other research projects from other academic institutions
Project visits
After hospital discharge there will be only one regular clinical visit 3 months after stroke onset in our neuroangiology ambulatorium with assessment of the NIHSS and mRS If a patient has deceased or is not able to come in person due to severe health restrictions the next of kin or caregiver in charge will be contacted to assess the degree of disability post stroke There will be no further clinical visits of telephone interviews planned within the study
Model predictions
The clinical and image data collected within this study will be transformed such that they can be fed into the DL models The DL models will already be trained that is they already learned the imaging features which are relevant for outcome prediction based on observational data that is not part of this study In the publication of the previously conducted research project in which we already used the method of the DL model prediction we describe in detail how the clinical and imaging data will be entered in the model and how the model itself is then run
When all the data is collected for the planned interim analysis or final analysis the models will be used to predict the outcome using the exact same patients to make the predictions comparable The treating physicians involved in the prediction part can have insight in the final results after data analysis and evaluation
Neurologists prediction of stroke outcome When a patient arrives at the stroke unit the treating team continuously integrates information as to what the best therapeutic options or risk of complications may be To predict patient outcomes neurologists rely on structured tabular data such as age sex and blood pressure and unstructured data such as medical image data Current prediction of therapeutic success in stroke patients outside the therapeutic time window is often based on brain images Joundi et al Neurology 97S68-S78 Diffusion and perfusion weighted imaging DWI PWI are used to identify the infarct core and hypoperfused region resulting in an estimate of the tissue at risk referred to as mismatchHeiss WD et al Int J Stroke 14351-8 Large mismatch and small infarct core are considered surrogate markers for treatment success which is why clinical trials have often pre-selected such patients Albers GW et al N Engl J Med 378708-18 However recent studies question the validity of such a pre-selection For instance novel pooled data analyses indicate that patients with large infarct core benefit from endovascular treatment as well Campbell BCW el a Lancet Neurol 1846-55 Moreover studies with the pre-selected patients do not allow drawing any conclusions about the effect of treatment in patients with a lack of mismatch or large cores It becomes increasingly clear that patients with a large core or lack of mismatch could benefit from endovascular thrombectomy treatment as well Karamchandani RR et al J Stroke Cerebrovasc Dis 31106548 We expect that a refined modelling approach and the integration of imaging and clinical parameters will yield better prognostic factors and more reliable outcome predictions
Computer aided prediction of stroke outcome For computer aided outcome prediction in stroke patients scores were developed based on a few preselected tabular features such as age and sex and an underlying logistic regression model While these methods slightly outperform the score-based methods they lack interpretability Therefore currently there are no computer aided outcome prediction tools used in clinical routine
Previous preparatory work In a previously conducted recent project weanalyzed imaging features from DWI and perfusion imaging data such as TTP CBFCBV and TMAX in a group of stroke patients with LVO all treated with MT at the InselSpital Berne We tested if the generally accepted mismatch concept derived from routine clinical software indeed enhances outcome prediction for individual patients We demonstrated that neither the core nor the mismatch volume significantly improved prediction of outcome when used in addition to clinical parameters Hamann J et al Eur J Neurol 281234-43 To implement a stroke outcome prediction model that is trustworthy for clinicians we developed deep learning DL based models that can not only integrate structured and unstructured data but also yield interpretable parameter estimates for clinical parameters like odds ratios httpswwwsciencedirectcomsciencearticleabspiiS003132032100443X We applied them to data from ischemic stroke patients treated at the University Hospital Berne httpsarxivorgabs220613302 and investigated if our models can compete with experienced neurologists by performing a blinded prediction challenge where five neurologists and our trained DL model were provided with the same structured clinical data of stroke patients In this experiment our model tended to show a better performance particularly when image data were provided Herzog L et al Stroke 2023 Jul5471761-1769
Accordingly the present STOP-stroke study aims to improve outcome prediction early after stroke by assessing both the clinicians outcome estimation as well as our trained DL model Thereby we try to implement a new prognostication tool which can be helpful in supporting treatment decision and individualized patient care
Primary and secondary endpoints in STOP-stroke
The primary endpoint of the the study will be the actual stroke related disability assessed by the mRS at 3 months as well as the NIHSS stroke severity after 24 hours and 3 months after acute ischemic stroke AIS Baseline factors that may have an influence on the primary endpoint are age sex vascular risk factors treatment times independence before stroke etc Secondary endpoints are the predicted mRS scores of the treating neurologists on the stroke unit one side and the predicted mRS by the DL model on the other
Project design
This is a single-center project conducted in the Department of Neurology at the USZ The study design will be exploratory
Recruitment screening and informed consent procedure
The location of recruitment will be the Department of Neurology at the USZ We plan to perform a single-center study All patients referred to the stroke unit of the USZ with suspicion of AIS from 7 am until 5 pm will be screened by the study team These patients will be recruited according to a consecutive ongoing recruitment through the study team in daily clinical practice The screening process will take place as described in the following section Patients with suspicion of AIS arriving at the stroke unit of the USZ will be screened if there is no refusal of data use already documented prior objected GC If yes these patients will not be considered to take part in the study If not an independent physician will be asked to consent for the patient in order not to delay the acute treatment The independent physician which can be any physician not part of the study team will be informed that their participation is voluntary and can be withdrawn at any time Patients will be asked for their consent as soon as they are able to consent in the course of the hospitalisation or at least until the routine 3-month follow up visit If until then the patient is still not able to consent we will ask the patients next of kin If the patient has no next of kin there is no contact to the next of kin or the patient is lost to follow-up or the patient has died data will be used if no objection against use of data for research is documented If patients andor next of kin are not able to appear in person for the routine visit we will send them a short letter of request of study participation together with the informed consent via mail Within the letter of request we will explain the informed consent procedure and ask patients either to send back the signed informed consent in case of study participation approval or to give a written statement of denyal of study participation We will inform patients within the letter of request that if there is no reply within four weeks we will use the obtained de-identified data if no prior objection against use of data for research is documented the document of the letter of request can be found as a separate attachment If patients reject to take part in the study the collected coded data will not be used for further data analysis
We developed an informed consent for patients next of kin and the independent physician specifically for the purpose of this study which is attached among the study documents submitted
Study procedures
The study is planned to start immediately after approval by the ethics committee and will continue for an overall project duration of approximately three years We plan a recruitment period of approximately one to two years The project duration for each patient will be three months as we assess the primary outcome parameter mRS three months after AIS As the study consists of a collection of personal health data of patients referred with suspicion of AIS to the USZ These clinical and imaging data will be de-identified and stored on a password secured USZ server We will not collect any biological patient material or samples blood or tissue
Clinical and imaging variables
We plan to collect routine laboratory and imaging data routine clinical scores of stroke severity and functional disability and other clinical routine information The treating attending physician board-certified neurologist on the stroke unit at the USZ will be asked on the time points N1 and N2 and the treating attending physician during the hospitalisation period on N3 to complete a questionnaire see attached at the end of this manuscript accessible only via personal login to a specifically for this project developed website planned with the secure web application REDCap The clinical and imaging parameters will be obtained from the treating physician on the stroke unit or the neurological ward of the USZ or can be extracted from the electronic patient chart KISIM We do not plan to extract data from the Swiss Stroke Registry SSR as the data from the acute setting will not be documented here at that time point The imaging data will be assessed at N2 from the clinically indicated first brain imaging
Additionally we will assess the following parameters by the treating physician on the stroke unit based on the medical history and clinical examination which we determined to be essential for the estimation of outcome frailty multimorbidity uncontrolled cancer pre-existing cognitive decline polypharmacy 4 medications institutional housing weak or no social support family or close friends and an uncategorized slot for up to three other variables not previously listed These parameters will be assessed categorically yesno option from the study team members upon questioning the treating attending physician or via assessment of the electronic patient chart The treating physician will assess the parameters upon clinical examination andor questioning the patient or next of kin if available in the acute setting A list of these parameters is attached at the end of this manuscript The web application REDCap will be designed by the Clinical Trial Center for the purpose of the project All treating attending physicians will receive a personalized login in order to complete the questionnaires for the different study time points directly in REDCap We plan a time-lock for the data entry to fix the data and secure the input of the data within reasonable time Access to the data in REDCap will have only the treating attending physician as well as the study team members The application is password secured for the purpose secure storage of study data
Outcome variables
NIHSS The National Institutes of Health Stroke Scale is a 42-point neurological exam used to quantify the severity of an acute stroke The NIHSS does assess the level of consciousness eye move-ments visual fields facial muscle function extremity strength sensory function coordination language speech and neglect The score will be collected as a standard procedure in AIS patients on admission to hospital at stroke onset and will represent one of the clinical parameters upon which the outcome prediction has to be made The NIHSS will be also a primary outcome parameter at 24 hours and 3 months after stroke onset which has to be predicted by the physicians on N1-3 and will be prospectively asked from the treating physicians on the stroke unit who are performing the score as part of the examination on admission routinely on N1 and asked from the treating neurologists on the ward on N3 which is part of the examination of discharge as well as at the routine 3-months control at the outpatient clinic of the USZ The study team will assess the score via the patient chart or directly from the treating physician of the outpatient clinic of the department of Neurology at the USZ If the patient is not able to present in person at the 3-month control the study team can contact the next of kin general practicioner treating physician or nurse in the rehabilitation center or nursing home in order to assess the score
mRS The modified Rankin Scale is a widely used scale of measuring the degree of disability of people who have suffered a stroke The mRS is used in clinical practice as well as in stroke studies to describe the clinical outcome after stroke The mRS runs from 0 to 6 with 0 being a patient without any disability and 6 indicating for death of the patient The score will be collected as a standard procedure in AIS patients on admission to hospital at stroke onset including the score before stroke onset as well as after three months as an outcome parameter assessed by physicians of the Department of Neurology The score will be prospectively asked on N1-3 as it also represents a primary outcome parameter as well as at the routine 3-months control at the outpatient clinic of the USZ The study team will assess the score at 3 months via the patient chart or from the treating physician of the outpatient clinic of the department of Neurology at the USZ at that time point If the patient is not able to present in person at the 3-month control the study team can contact the next of kin general practicioner treating physician or nurse in the rehabilitation center or nursing home in order to assess the score
Imaging data
We plan to store the imaging data CT and MR images of the brain including CT- and MR-angiography as well as perfusion images of the brain collected on admission to USZ or the hospital of the initial hospitalization as part of the clinically indicated first neuroimaging performed after admission to hospital with suspicion of acute ischemic stroke The imaging data will be taken from the imaging software system used at USZ The de-identification process will be performed according to USZ internal standards A password protected master file will be stored by the data manager on USZ servers which are secured according to USZ guidelines Images will be stored in a de-identified form in a project specific area with restricted access Access will be granted to all project members according to the staff list As data will be archived in a structured way and stored in a coded form imaging data can be made available on reasonable request for other research projects from other academic institutions
Project visits
After hospital discharge there will be only one regular clinical visit 3 months after stroke onset in our neuroangiology ambulatorium with assessment of the NIHSS and mRS If a patient has deceased or is not able to come in person due to severe health restrictions the next of kin or caregiver in charge will be contacted to assess the degree of disability post stroke There will be no further clinical visits of telephone interviews planned within the study
Model predictions
The clinical and image data collected within this study will be transformed such that they can be fed into the DL models The DL models will already be trained that is they already learned the imaging features which are relevant for outcome prediction based on observational data that is not part of this study In the publication of the previously conducted research project in which we already used the method of the DL model prediction we describe in detail how the clinical and imaging data will be entered in the model and how the model itself is then run
When all the data is collected for the planned interim analysis or final analysis the models will be used to predict the outcome using the exact same patients to make the predictions comparable The treating physicians involved in the prediction part can have insight in the final results after data analysis and evaluation
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None