Public Workshops - Diagnostic and Monitoring Medical Devices for Opioid Use and Risk Prediction Devices of Opioid Use and Opioid Use Disorder - Opportunities and Challenges
CDRH Notice Public Workshops Diagnostic and Monitoring Medical Devices for Opioid Use and Risk Prediction Devices of Opioid Use and Opioid Use Disorder Opportunities and Challenges Open
2022-12-08 2022-12-08 2022-12-08 2022-12-08 2022-12-08 2022-11-23 2022-11-10 2022-12-06 2022-12-08
FDA-2022-N-2393-0008 FDA-2022-N-2393-0006 FDA-2022-N-2393-0015 FDA-2022-N-2393-0010 FDA-2022-N-2393-0016 FDA-2022-N-2393-0004 FDA-2022-N-2393-0002 FDA-2022-N-2393-0005 FDA-2022-N-2393-0009
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https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0008 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0006 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0015 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0010 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0016 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0004 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0002 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0005 https://api.regulations.gov/v4/comments/FDA-2022-N-2393-0009
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This is a hard no for me. Let Drs do what they think is best, not some machine telling them. Anti-opoiod rhetoric and targeting has killed many. People are left with death as the only option to escape chronic pain. Stay out of our Drs appointments..this machine will inadvertently cause even more deaths.. I was told in April of 2019 the pain management practice I was seeing that I was being cut off meds. I remembered very well the pain I will have until I die would be back. Said stimulator was only thing could help. I saw doctor Id not seen before in the over 4 years of being there. They made sure I got the meds for that, and in Aug of 2019 I got the permanent one and they were cutting my meds off. It was a domino effect. I know need helpers. I dont see where a device that can tell what your pain is useless. When I gave birth to my 2nd son they used a device to keep track of contractions. Didnt work, kept telling them I was having closer pain and they looked at machine and said I was fine. Finally they looked and my son was on his way out. I believe only the patient can tell you how much pain you have. A new gadget that can tell is just another medical expense that isnt necessary. We are human and places have seem to forgot that. Insurance spent thousands on the stimulator, which I believe is a waste of money. Since so many doctors have turned to them, rate of success isnt as good. Any extra equipment to put us through that doctors need to use is just another insurance waste like my stimulator was. Would be a lot better to go back to what works for us. And keeping our records private as well. It is fast become inhumane treatment as it is. I feel its a waste of money for insurance companies have to pay. Are doctors allowed to listen to patient anymore. I know what my pain is. Please stop trying more fixer uppers and technology. Insurance paid to get stimulator out. Please no more trusting a machine to tell us about our pain. Its ridiculous. So let me get this straight: you want to subject me to a surgical procedure to prove that my pain is real? Who pays for this device? Who monitors it? What happens if this device breaks or malfunctions? As a person who suffers from chronic pain on a daily basis all because someone at the insurance company didnt feel my fractured spine was important enough to do a spinal fusion, do you really think I would trust anyone else doing unnecessary procedures to my body? After three tries with the insurance company, I finally had my fusion and guess what? My spine was in 2 pieces and my nerves were exposed and flapping. This is not a life I would wish on anyone.  To push CPPs to do monthly drug tests, pill counts, sign contracts, and be treated like criminals; only to now be pushed to get a medical devise  just to prove Im in pain is never going to happen. This is just another get rich quick scam the medical community will push, but it wont work. We are not criminals. Enough is enough. I am appalled at the lack of diversity of pain patients that were used. Valuable time was wasted playing prerecorded statement. There was a significant amount of duplicity.  I am even more disturbed that there was not more discussion about how patients on LTOT are profiled and denied opioids even in acute pain while in hospital from Narxcare. A score that is shielded by privacy under argument that its a proprietary algorithm. Yet FDA is testing the water to add more computer driven apparatus without transparency. DEA shouldnt be able to troll PDMPs an then use parallel construction to create cases against Drs. FDA needs to address the elephant in the room that opioids are FDA approved but not allowed under DEA scrutiny.  We (experts in statistical and psychiatric genetics, including substance use disorders) wish to raise important issues related both to the general topic and to a specific test that was described during the workshop. We highlight the expert consensus on 2 general issues about genetics of psychiatric and substance use disorders most relevant to the workshop and to potential approval of devices for risk prediction.First, while genetic variations contribute to the risk for psychiatric and substance use disorders (SUD), the genetic contribution accounts for only about half of the variation in risk, and that portion is distributed among hundreds to thousands of genes. The idea that a small set of genetic variants, however combined, can distinguish those at elevated risk for an SUD simply does not align with accepted scientific fact regarding the genetics of SUD and other psychiatric disorders.Second, there needs to be control for the participants genetic ancestry in any test for a trait or disorder that is complex (i.e., due to the effects of many genes). Groups of people differ in the frequencies of many genetic variants, and differences often track with recent ancestry. If these differences are not accounted for, they result in spurious findings where genetic variants that are actually indicators of the ancestry of a group can show up as false positive associations with the trait (e.g. the chopstick gene, PMID: 10673763). This is a major risk, because if a medical test preferentially confounds ancestry with OUD risk, it can contribute to biased treatment. During the workshop, one particular test that is being evaluated by the FDA was described: the AvertD(TM) genetic test for Opioid Use Disorder (OUD) risk, from SOLVD Health. We think that it is an example of a flawed test that could generate harm, and serves as an example of issues that should be considered in evaluating any such test.The AvertD(TM) test purports to predict who is at risk for becoming opioid dependent if prescribed opioids. The company proposes that a practitioner could test patients to determine risk before prescribing opioids, and use the information to guide his/her prescribing. The test uses 15 variants (PMID: 34452883); a prior version (PMID: 28801372) used those plus one additional. Based on the largest genome-wide studies to date (samples range from approx. 80,000 to 400,000; e.g. PMID: 35879402; PMID: 36171425; PMID: 34728798), even a full genomes worth of markers is not currently able to predict an individuals risk for OUD in a clinically useful way.One group evaluated how ignoring the scientific consensus noted above results in bias (PMID: 34710714), with examples using the exact genetic markers used previously by the creators of the test. The proposed SNPs differed significantly in allele frequency among different populations, a major problem. Although the companys particular machine-learning algorithm is not public, the group tested 5 major types of machine learning algorithms with the SNPs the company chose, as well as with 8 random sets of SNPs that were matched to theirs on allele frequency. These SNPs and algorithms were tested in a large number of carefully evaluated and ethnically matched individuals: half of European American and half of African American genetic ancestry, and within each group half of the participants met criteria for strictly defined OUD (DSM-IV) and half did not. The paper showed there was no predictive utility for OUD attributable to these SNPs. When done incorrectly the prediction could appear to exceed 90% accuracy but also  alarmingly  the prediction by this set of variants was highly confounded by ancestry such that it predicted subpopulations of minorities rather than OUD. This is particularly problematic, because it could exacerbate current biases in pain treatment. That paper also tested simulated data and another complex phenotype, tobacco smoking, and our results generalized. These analyses serve as a cautionary note when evaluating any genetic test for an SUD.We urge your panel to carefully review the consensus on the genetics of OUD and the published work on algorithmic bias in genetic prediction of opioid use disorder in any decisions on such a predictive test.A bibliography is appended as a pdf, due to space limitations. This device to tell doctors if a patient needs pain medication is asinine. Its a horrible idea.  Ive been on long term opiate therapy for 30 years. Opiates have saved my life. Without them I was unable to get out of bed without great pain and difficulty.  When I was forced off opiate therapy because of the anti opiate hysteria it almost killed me. I ended up in the ER and a stay in the hospital for a week to stabilize my heart. This should never happen to another patient. Ever and putting a device on someone to measure how much pain they are in instead of just listening to the patient is ridiculous. Same with spinal cord stimulators. Another piece of medical quackery  I am a data scientist with over 20 years of experience and have been affected by addiction.  The presentations of A. Haotum claiming there is scientific consensus that his approach is correct is demonstrably incorrect.  I reviewed the entirety of his published work including the papers he referred two in his short presentation.   None of the papers follow best practices in our space. All papers contain numerous flaws in study design that have been shown to lead to invalid modeling and incorrect results. For example, the in the study he spent the most time discussing, the inter-rater Cohen kappa coefficients the study data for the survey instrument (Semi-Structured Assessment for Drug Dependence and Alcoholism) used to classify opioid dependency range from ~0.1 to 0.4. This is important, because in electronically derived predictive classification modeling, we need to understand the noise in our data.  It is a reasonable assumption that the Cohen kappas contribute to the class noise and therefore set the minimal amount of class noise in opioid dependency of the dataset within this range (i.e. the minimal amount of class noise in opioid dependency in your study would be ~10-40%).  Whereas the empirically defined ancestry classification he used would not have equal class noise levels (although as you know the use of the 1000 genome data to assign ancestry and the under-specification issues with the genetic attributes (MAFs) in this approach is concerning especially for the AA groups).  Given that the approaches used will degrade with as little as 5% class noise, and that the differences in class noise of at least 10-40%, the comparative analysis using the unadjusted/uncleaned data with the differences in class noise between the two classes studied in invalid and uninterpretable.  Numerous independent studies have shown using uncleaned/unadjusted data across apparently large difference in class noise between classes (opioid dependency and ancestry in this case) makes the modeling, comparisons, and the conclusions unsupported. In addition, I also have a significant concern in the way the study is designed to test hypotheses within a dataset across classes and then generalized externally.  A reasonable interpretation of the results is that this dataset is not adequate to use for modeling opioid dependency, but that all that would be needed for this interpretation is to look at the inter-rater kappas for the class (it took me literally 10 min). Trying to generalize the limitations of the dataset to attributes or studies outside this dataset is utter nonsense. Please read the attachment from the Oregon Pain Action Group which contains our comment. Thank you.  I writing this today to voice my concerns about the manner chronic pain patients are treated nowadays. Since the DEA has been threatening our Doctors, we have been forced into pain clinics where the Doctors have really no knowledge of of our history or any condition that our PCP would know right off the top of his head! Its criminal that they are allowed to over ride our Doctors and in some cases put them in jail for only prescribing medication to their patients! In fact they are using this so called opioid crisis to do this! If truth be told 87% of deaths are from fentanyl that flows In across our Southrren border. Something must be done to help my community of chronic pain patients and deal with the illegal drugs pooring in