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1. Digital Twin Generators for Disease Modeling

Author

Alam, Nameyeh, Basilico, Jake, Bertolini, Daniele, Chetty, Satish Casie, D'Angelo, Heather, Douglas, Ryan, Fisher, Charles K., Fuller, Franklin, Gomes, Melissa, Gupta, Rishabh, Lang, Alex, Loukianov, Anton, Mak-McCully, Rachel, Murray, Cary, Pham, Hanalei, Qiao, Susanna, Ryapolova-Webb, Elena, Smith, Aaron, Theoharatos, Dimitri, Tolwani, Anil, Tramel, Eric W., Vidovszky, Anna, Viduya, Judy, and Walsh, Jonathan R.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

A patient's digital twin is a computational model that describes the evolution of their health over time. Digital twins have the potential to revolutionize medicine by enabling individual-level computer simulations of human health, which can be used to conduct more efficient clinical trials or to recommend personalized treatment options. Due to the overwhelming complexity of human biology, machine learning approaches that leverage large datasets of historical patients' longitudinal health records to generate patients' digital twins are more tractable than potential mechanistic models. In this manuscript, we describe a neural network architecture that can learn conditional generative models of clinical trajectories, which we call Digital Twin Generators (DTGs), that can create digital twins of individual patients. We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters. By introducing a general purpose architecture, we aim to unlock the ability to scale machine learning approaches to larger datasets and across more indications so that a digital twin could be created for any patient in the world.

Published
2024

2. Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials

Author

Li, Yunfan, Sabbaghi, Arman, Walsh, Jonathan R., and Fisher, Charles K.

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Machine Learning, 62J12
Abstract

Randomized controlled trials (RCTs) with binary primary endpoints introduce novel challenges for inferring the causal effects of treatments. The most significant challenge is non-collapsibility, in which the conditional odds ratio estimand under covariate adjustment differs from the unconditional estimand in the logistic regression analysis of RCT data. This issue gives rise to apparent paradoxes, such as the variance of the estimator for the conditional odds ratio from a covariate-adjusted model being greater than the variance of the estimator from the unadjusted model. We address this challenge in the context of adjustment based on predictions of control outcomes from generative artificial intelligence (AI) algorithms, which are referred to as prognostic scores. We demonstrate that prognostic score adjustment in logistic regression increases the power of the Wald test for the conditional odds ratio under a fixed sample size, or alternatively reduces the necessary sample size to achieve a desired power, compared to the unadjusted analysis. We derive formulae for prospective calculations of the power gain and sample size reduction that can result from adjustment for the prognostic score. Furthermore, we utilize g-computation to expand the scope of prognostic score adjustment to inferences on the marginal risk difference, relative risk, and odds ratio estimands. We demonstrate the validity of our formulae via extensive simulation studies that encompass different types of logistic regression model specifications. Our simulation studies also indicate how prognostic score adjustment can reduce the variance of g-computation estimators for the marginal estimands while maintaining frequentist properties such as asymptotic unbiasedness and Type I error rate control. Our methodology can ultimately enable more definitive and conclusive analyses for RCTs with binary primary endpoints., Comment: 27 pages, 1 figure, 9 tables

Published
2024

3. Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors

Author

Vanderbeek, Alyssa M., Sabbaghi, Arman, Walsh, Jon R., and Fisher, Charles K.

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Machine Learning, 62F15
Abstract

Effective and rapid decision-making from randomized controlled trials (RCTs) requires unbiased and precise treatment effect inferences. Two strategies to address this requirement are to adjust for covariates that are highly correlated with the outcome, and to leverage historical control information via Bayes' theorem. We propose a new Bayesian prognostic covariate adjustment methodology, referred to as Bayesian PROCOVA, that combines these two strategies. Covariate adjustment in Bayesian PROCOVA is based on generative artificial intelligence (AI) algorithms that construct a digital twin generator (DTG) for RCT participants. The DTG is trained on historical control data and yields a digital twin (DT) probability distribution for each RCT participant's outcome under the control treatment. The expectation of the DT distribution, referred to as the prognostic score, defines the covariate for adjustment. Historical control information is leveraged via an additive mixture prior with two components: an informative prior probability distribution specified based on historical control data, and a weakly informative prior distribution. The mixture weight determines the extent to which posterior inferences are drawn from the informative component, versus the weakly informative component. This weight has a prior distribution as well, and so the entire additive mixture prior is completely pre-specifiable without involving any RCT information. We establish an efficient Gibbs algorithm for sampling from the posterior distribution, and derive closed-form expressions for the posterior mean and variance of the treatment effect parameter conditional on the weight, in Bayesian PROCOVA. We evaluate efficiency gains of Bayesian PROCOVA via its bias control and variance reduction compared to frequentist PROCOVA in simulation studies that encompass different discrepancies. These gains translate to smaller RCTs., Comment: 33 pages, 12 figures, 2 tables; Added a new appendix section (conclusions unchanged)

Published
2023

4. A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials

Author

Vanderbeek, Alyssa M., Vidovszky, Anna A., Ross, Jessica L., Sabbaghi, Arman, Walsh, Jonathan R., Fisher, Charles K., Disease, the Critical Path for Alzheimer's, Initiative, the Alzheimer's Disease Neuroimaging, Disease, the European Prevention of Alzheimer's, Consortium, and Study, the Alzheimer's Disease Cooperative

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Machine Learning, 62J99
Abstract

A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful treatment effect inferences is to incorporate predictions from generative artificial intelligence (AI) algorithms into covariate adjustment for the regression analysis of a RCT. Training a generative AI algorithm on historical control data enables one to construct a digital twin generator (DTG) for RCT participants, which utilizes a participant's baseline covariates to generate a probability distribution for their potential control outcome. Summaries of the probability distribution from the DTG are highly predictive of the trial outcome, and adjusting for these features via regression can thus improve the quality of treatment effect inferences, while satisfying regulatory guidelines on statistical analyses, for a RCT. However, a critical assumption in this strategy is homoskedasticity, or constant variance of the outcome conditional on the covariates. In the case of heteroskedasticity, existing covariate adjustment methods yield inefficient estimators and underpowered tests. We propose to address heteroskedasticity via a weighted prognostic covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the mean and variance of the regression model using information obtained from the DTG. We prove that our method yields unbiased treatment effect estimators, and demonstrate via comprehensive simulation studies and case studies from Alzheimer's disease that it can reduce the variance of the treatment effect estimator, maintain the Type I error rate, and increase the power of the test for the treatment effect from 80% to 85%~90% when the variances from the DTG can explain 5%~10% of the variation in the RCT participants' outcomes., Comment: 49 pages, 6 figures, 12 tables

Published
2023

5. A Rule of Thumb for the Power Gain due to Covariate Adjustment in Randomized Controlled Trials with Continuous Outcomes

Author

Fisher, Charles K.

Subjects
Statistics - Methodology
Abstract

Randomized Controlled Trials (RCTs) often adjust for baseline covariates in order to increase power. This technical note provides a short derivation of a simple rule of thumb for approximating the ratio of the power of an adjusted analysis to that of an unadjusted analysis. Specifically, if the unadjusted analysis is powered to approximately 80\%, then the ratio of the power of the adjusted analysis to the power of the unadjusted analysis is approximately $1 + \frac{1}{2} R^2$, where $R$ is the correlation between the baseline covariate and the outcome.

Published
2023

6. Neural Boltzmann Machines

Author

Lang, Alex H., Loukianov, Anton D., and Fisher, Charles K.

Subjects
Computer Science - Machine Learning, Condensed Matter - Disordered Systems and Neural Networks, Statistics - Machine Learning
Abstract

Conditional generative models are capable of using contextual information as input to create new imaginative outputs. Conditional Restricted Boltzmann Machines (CRBMs) are one class of conditional generative models that have proven to be especially adept at modeling noisy discrete or continuous data, but the lack of expressivity in CRBMs have limited their widespread adoption. Here we introduce Neural Boltzmann Machines (NBMs) which generalize CRBMs by converting each of the CRBM parameters to their own neural networks that are allowed to be functions of the conditional inputs. NBMs are highly flexible conditional generative models that can be trained via stochastic gradient descent to approximately maximize the log-likelihood of the data. We demonstrate the utility of NBMs especially with normally distributed data which has historically caused problems for Gaussian-Bernoulli CRBMs. Code to reproduce our results can be found at https://github.com/unlearnai/neural-boltzmann-machines., Comment: 7 pages, 4 figures

Published
2023

9. Can RBMs be trained with zero step contrastive divergence?

Author

Fisher, Charles K.

Subjects
Computer Science - Machine Learning
Abstract

Restricted Boltzmann Machines (RBMs) are probabilistic generative models that can be trained by maximum likelihood in principle, but are usually trained by an approximate algorithm called Contrastive Divergence (CD) in practice. In general, a CD-k algorithm estimates an average with respect to the model distribution using a sample obtained from a k-step Markov Chain Monte Carlo Algorithm (e.g., block Gibbs sampling) starting from some initial configuration. Choices of k typically vary from 1 to 100. This technical report explores if it's possible to leverage a simple approximate sampling algorithm with a modified version of CD in order to train an RBM with k=0. As usual, the method is illustrated on MNIST.

Published
2022

10. Effectiveness of prophylactic intranasal photodynamic disinfection therapy and chlorhexidine gluconate body wipes for surgical site infection prophylaxis in adult spine surgery

Author

Moskven, Eryck, Banaszek, Daniel, Sayre, Eric C., Gara, Aleksandra, Bryce, Elizabeth, Wong, Titus, Ailon, Tamir, Charest-Morin, Raphaele, Dea, Nicolas, Dvorak, Marcel F., Fisher, Charles G., Kwon, Brian K., Paquette, Scott, and Street, John T.

Subjects
Infection control -- Usage -- Health aspects -- Analysis, Medical research -- Analysis -- Usage -- Health aspects, Medicine, Experimental -- Analysis -- Usage -- Health aspects, Methylene blue -- Usage, Patient compliance -- Analysis -- Usage -- Health aspects, Comorbidity -- Prevention, Drug resistance in microorganisms -- Prevention, Mupirocin -- Usage, Infection -- Prevention, Methicillin -- Usage, Adults -- Health aspects -- Analysis -- Usage, Health, Health care industry
Abstract

Background: Current measures to prevent spinal surgical site infection (SSI) lack compliance and lead to antimicrobial resistance. We aimed to examine the effectiveness of bundled preoperative intranasal photodynamic disinfection therapy (nPDT) and chlorhexidine gluconate (CHG) body wipes in the prophylaxis of spine SSIs in adults, as well as determine our institutional savings attributable to the use of this strategy and identify adverse events reported with nPDT-CHG. Methods: We performed a 14-year prospective observational interrupted time-series study in adult (age > 18 yr) patients undergoing emergent or elective spine surgery with 3 time-specific cohorts: before rollout of our institution's nPDT-CHG program (2006-2010), during rollout (2011-2014) and after rollout (2015-2019). We used unadjusted bivariate analysis to test for temporal changes across patient and surgical variables, and segmented regression to estimate the effect of nPDT-CHG on the annual SSI incidence rates per period. We used 2 models to estimate the cost of nPDT-CHG to prevent 1 additional SSI per year and the annual cumulative cost savings through SSI prevention. Results: Over the study period, 13 493 patients (mean 964 per year) underwent elective or emergent spine surgery. From 2006 to 2019, the mean age, mean Charlson Comorbidity Index (CCI) score and mean Spine Surgical Invasiveness Index (SSII) score increased from 48.4 to 58.1 years, from 1.7 to 2.6, and from 15.4 to 20.5, respectively (p < 0.001). Unadjusted analysis confirmed a significant decrease in the annual number (74.6 to 26.8) and incidence (7.98% to 2.67%) of SSIs with nPDT-CHG (p < 0.001). After adjustment for mean age, mean CCI score and mean SSII score, segmented regression showed an absolute reduction in the annual SSI incidence rate of 3.36% per year (p < 0.001). The estimated annual cost to prevent 1 additional SSI per year was about $1350-$1650, and the estimated annual cumulative cost savings were $2 484856-S2 495 016. No adverse events were reported with nPDT-CHG. Conclusion: Preoperative nPDT-CHG administration is an effective prophylactic strategy for spinal SSIs, with significant cost savings. Given its rapid action, minimal risk of antimicrobial resistance, broad-spectrum activity and high compliance rate, preoperative nPDT-CHG decolonization should be the standard of care for all patients undergoing emergent or elective spine surgery. Contexte : L'application des mesures de prévention des infections des plaies opératoires (IPO) du rachis laisse actuellement à désirer et pourrait contribuer à la résistance aux antibiotiques. Nous avons voulu vérifier l'efficacité de la thérapie photodynamique antimicrobienne (TPA) intranasale préopératoire alliée à l'utilisation de lingettes désinfectantes à la chlorhexidine (LDC) pour le corps en prophylaxie des IPO du rachis chez des adultes, calculer les économies réalisées par notre établissement grâce à la stratégie TPD-LDC et recenser ses effets indésirables. Méthodes : Nous avons procédé à une étude observationnelle de séries chronologiques interrompues échelonnée sur une période de 14 ans chez des adultes (âgés de >18 ans) soumis à une chirurgie urgente ou non urgente du rachis auprès de 3 cohortes chronologiques spécifiques : avant le déploiement de la stratégie dans notre établissement (2006-2010), puis durant (2011-2014) et après son déploiement (2015-2019). Nous avons appliqué une analyse bivariée non ajustée pour vérifier les changements dans le temps selon la patientèle et les paramètres chirurgicaux, et segmenté la régression pour estimer l'effet de la stratégie TPA-LDC sur les taux d'incidence annuels moyens d'IPO par période. Nous avons utilisé 2 modèles pour estimer le coût de la stratégie TPA-LDC apte à prévenir une IPO additionnelle par année et les économies cumulatives annuelles moyennes générées par la prévention des IPO. Résultats : Durant la période de l'étude, 13 493 malades (964 par année en moyenne) ont subi une chirurgie urgente ou non urgente du rachis. Entre 2006 et 2011 et entre 2015 et 2019, l'âge moyen, l'indice moyen de comorbidité de Charlson (ICC) et l'indice moyen d'invasivité de la chirurgie du rachis (IICR) sont passés de 48,4 à 58,1 ans, de 1,7 à 2,6 et de 15,4 à 20,5, respectivement (p < 0,001). L'analyse non ajustée a confirmé une baisse significative du nombre annuel (de 74,6 à 26,8) et de l'incidence (de 7,98% à 2,67%) des IPO associée à la stratégie TPA-LDC (p < 0,001). Après ajustement pour tenir compte de l'âge, de l'ICC et de l'IICR moyens, la régression segmentée a montré une réduction absolue du taux d'incidence annuel des IPO de 3,36% par année (p < 0,001). Le coût annuel moyen estimé pour prévenir une IPO additionnelle a été d'environ 1350$-1650$, et les économies cumulatives annuelles moyennes estimées ont été de 2484856 $-2 495 016$. Aucun effet indésirable n'a été signalé avec la stratégie TPA-LDC. Conclusion : L'application préopératoire de la stratégie TPA-LDC est une mesure prophylactique efficace pour les IPO du rachis, et donne lieu à des économies significatives. Compte tenu de sa rapidité d'action, du risque minimal de résistance aux antibiotiques, de son activité à large spectre et du taux d'observance élevé, la décolonisation préopératoire par TPA-LDC devrait être une norme de soins pour l'ensemble de la patientèle soumise à une chirurgie urgente ou non urgente du rachis., Surgical site infections (SSIs) are devastating postoperative complications associated with substantial morbidity, prolonged length of stay (LOS) and increased health care-related costs. (1) The rate of SSIs after spine surgery [...]

Published
2023
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13. Temporal analysis of complication rates of cervical spine surgery for degenerative spine disease between younger and older cohorts using the CSORN registry: Is age just a number?

Author

Ajoku, Uchenna, Johnson, Michael G., McIntosh, Greg, Thomas, Ken, Bailey, Christopher S., Hall, Hamilton, Fisher, Charles G., Manson, Neil, Rampersaud, Y. Raja, Dea, Nicolas, Christie, Sean, Abraham, Edward, Weber, Michael H., Charest-Morin, Raphaele, Attabib, Najmedden, le Roux, André, Phan, Philippe, Paquet, Jerome, Lewkonia, Peter, and Goytan, Michael

Published
2023
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14. Preoperative expectations of patients with degenerative cervical myelopathy: an observational study from the Canadian Spine Outcomes and Research Network

Author

Althagafi, Alwalaa, Dea, Nicolas, Evaniew, Nathan, Rampersaud, Raja Y., Jacobs, W. Bradley, Paquet, Jérome, Wilson, Jefferson R., Hall, Hamilton, Bailey, Christopher S., Weber, Michael H., Nataraj, Andrew, Attabib, Najmedden, Cadotte, David W., Phan, Philippe, Christie, Sean D., Fisher, Charles G., Manson, Neil, Thomas, Kenneth, McIntosh, Greg, and Charest-Morin, Raphaële

Published
2024
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16. Cost consequence analysis of waiting for lumbar disc herniation surgery

Author

Dandurand, Charlotte, Mashayekhi, Mohammad Sadegh, McIntosh, Greg, Singh, Supriya, Paquet, Jerome, Chaudhry, Hasaan, Abraham, Edward, Bailey, Christopher S., Weber, Michael H., Johnson, Michael G., Nataraj, Andrew, Attabib, Najmedden, Kelly, Adrienne, Hall, Hamilton, Rampersaud, Y. Raja, Manson, Neil, Phan, Philippe, Thomas, Ken, Fisher, Charles, Charest-Morin, Raphaele, Soroceanu, Alex, LaRue, Bernard, and Dea, Nicolas

Published
2023
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17. Degenerative spinal conditions requiring emergency surgery: an evolving crisis in a publicly funded health care system

Author

Dandurand, Charlotte, Hindi, Mathew N., Laghaei, Pedram Farimani, Mashayekhi, Mohammad Sadegh, Kwon, Brian K., Dea, Nicolas, Fisher, Charles G., Charest-Morin, Raphaele, Ailon, Tamir, Boyd, Michael, Dvorak, Marcel, Paquette, Scott, and Street, John

Subjects
Surgical emergencies -- Statistics, Spinal diseases -- Statistics -- Care and treatment -- Patient outcomes, Spine -- Surgery, Degeneration (Pathology) -- Statistics -- Care and treatment -- Patient outcomes, Health, Health care industry
Abstract

Background: Surgery for degenerative spine pathologies is typically performed on a scheduled basis; however, worsening symptoms may warrant emergency surgery. An increasing number of patients requiring emergency surgery has been observed (22.6% in 2006 to 34.8% in 2019). We sought to compare the outcomes of patients who received scheduled surgery and those who required emergency surgery. Methods: All patients treated between Jan. 1, 2006, and Dec. 31, 2019, were included. Retrospective comparisons were made between patients who were scheduled (elective) for surgery and those requiring emergency surgery, patients who were scheduled for surgery and those who decompensated while on the surgical waitlist and patients who presented as de novo emergencies and those who decompensated while on the surgical waitlist. Results: Among the 6217 patients with degenerative pathologies, 4654 (74.9%) patients were scheduled (elective) for surgery and 1563 (25.1%) were patients requiring emergency surgery. Compared with patients who were scheduled, patients requiring emergency surgery had a longer length of stay (LOS) in hospital (5.1 d, interquartile range [IQR] 2.7-11.2 v. 3.6 d, IQR 1.3-6.4, p < 0.001) and lower rate of home discharge (78.6% v. 94.2%, p < 0.001). Patients requiring emergency surgery were 1.34 times more likely to have any adverse events (95% confidence interval [CI] 1.06-1.68, p = 0.01). When compared with patients who were scheduled for surgery, those who decompensated while on the surgical waitlist had longer LOS (7.0 d, IQR 3.3-15.0 v. 3.6 d, IQR 1.3-6.4, p < 0.001), less home discharge (77.6% v. 94.2%, p < 0.001) and were 2.5 times more likely to have any adverse events (95% CI 1.5-4.1, p < 0.001). Patients who decompensated had a 2.1 times higher risk of having any adverse events than patients who presented as de novo emergencies (95% CI 1.2-3.6, p< 0.001). Conclusion: We observed worse perioperative outcomes for patients requiring emergency surgery for degenerative spinal conditions than for patients who were scheduled for surgery. Patients who decompensated while on the surgical waitlist had the worst outcomes. Contexte: Le traitement chirurgical des pathologies dégénératives de la colonne vertébrale est généralement planifié; l'aggravation des symptômes peut toutefois justifier une opération d'urgence. On constate d'ailleurs qu'un pourcentage croissant de patients ont besoin d'une telle intervention (22,6% en 2006 comparativement à 34,8% en 2019). Nous avons cherché à comparer les issues des patients ayant subi une intervention planifiée avec celles des patients nécessitant une opération d'urgence. Méthodes: Tous les patients traités entre le 1er janvier 2006 et le 31 décembre 2019 ont été inclus. Nous avons établi des comparaisons rétrospectives entre les patients allant subir une intervention planifiée (non urgente) et ceux ayant besoin d'une opération d'urgence, entre les patients allant subir une intervention planifiée et ceux ayant subi une décompensation alors qu'ils étaient sur une liste d'attente, et entre les patients nécessitant une opération urgente pour une pathologie de novo et ceux ayant subi une décompensation alors qu'ils étaient sur une liste d'attente. Résultats: Parmi les 6217 patients présentant des pathologies dégénératives, 4654 (74,9%) allaient subir une intervention planifiée (non urgente) et 1563 (25,1%) avaient besoin d'une opération d'urgence. Comparativement aux premiers, ces derniers restaient plus longtemps à l'hôpital (5,1 jours [écart interquartile (EI) 2,7-11,2] c. 3,6 jours [EI 1,3-6,4], p < 0,001) et présentaient un taux de congé plus faible (78,6% c. 94,2%, p < 0,001). Ils étaient 1,34 fois plus susceptibles d'être victimes d'un événement indésirable (intervalle de confiance [IC] de 95% 1,06-1,68, p = 0,01). Comparativement aux patients pour lesquels une intervention était prévue, ceux ayant subi une décompensation alors qu'ils étaient sur une liste d'attente restaient plus longtemps à l'hôpital (7,0 jours [EI 3,3-15,0] c. 3,6 jours [EI 1,3-6,4], p < 0,001), présentaient un taux de congé plus faible (77,6% c. 94,2%, p < 0,001) et étaient 2,5 fois plus susceptibles de subir un événement indésirable (IC de 95% 1,5-4,1, p < 0,001). Les patients ayant subi une décompensation présentaient un risque 2,1 fois plus élevé d'être victimes d'un événement indésirable, comparativement aux patients nécessitant une opération urgente pour une pathologie de novo (IC de 95% 1,2-3,6, p < 0,001). Conclusion: Nous avons observé des issues périopératoires moins favorables chez les patients ayant besoin de se faire opérer d'urgence pour des pathologies dégénératives de la colonne vertébrale, comparativement à ceux pour lesquels une intervention était prévue. Les patients ayant subi une décompensation alors qu'ils étaient sur une liste d'attente présentaient les pires issues., Older age demographics have increased the demand for spinal surgery, creating an ever-increasing burden for the health care system. (1) This is especially true for degenerative spinal pathologies such as [...]

Published
2023
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19. Development of the cervical myelopathy severity index: a new patient reported outcome measure to quantify impairments and functional limitations

Author

Malhotra, Armaan K., He, Yingshi, Harrington, Erin M., Jaja, Blessing N.R., Zhu, Mary P., Shakil, Husain, Dea, Nicolas, Weber, Michael H., Attabib, Najmedden, Phan, Philippe, Rampersaud, Yoga Raja, Paquet, Jerome, Jacobs, W. Bradley, Cadotte, David W., Christie, Sean D., Nataraj, Andrew, Bailey, Christopher S., Johnson, Michael, Fisher, Charles, Hall, Hamilton, Manson, Neil, Thomas, Kenneth, Ginsberg, Howard J., Fehlings, Michael G., Witiw, Christopher D., Davis, Aileen M., and Wilson, Jefferson R.

Published
2024
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20. PLCG2-associated immune dysregulation (PLAID) comprises broad and distinct clinical presentations related to functional classes of genetic variants

Author

Abbott, Jordan K., Aldave Becerra, Juan Carlos, Allenspach, Eric J., Assing, Kristian, Atkinson, T. Prescott, Bargir, Umair A., Baxter, Sarah K., Bergerson, Jenna R.E., Bista, Ranjan, Blanche, Stephane, Buckley, Lenore M., Butte, Manish, Carcamo, Benjamin, Chandrakala, Shanmukhaiah, Chen, Karin, Chervinskiy, Sheva, Chinn, Ivan K., Chong, Hey J., Coffey, Kara E., Copland, Andrew P., Cowen, Edward W., Cros, Guilhem, De Bruycker, Jean Jacques, Teresa de la Morena, Maria, Ehlayel, Mohammed, Forbes Satter, Lisa R., Gelfand, Erwin W., Gilliaux, Olivier, Glover, Sara C., Gorman, Mark, Griffin, Thomas A., Grimbacher, Bodo, Gru, Alejandro A., Haddad, Elie, Hadjadj, Jerome, Hajjar, Joud, Hauck, Fabian, Hautala, Timo, Holland, Steven M., Hsieh, Elena W.Y., Hsu, Florence Ida, Jacquemin, Emmanuel, Jindal, Ankur Kumar, Kahn, Stacy A., Keller, Michael D., Kobayashi, Roger H., Krupski, Christa, Larkin, Allyson, Lawrence, Monica G., Madkaikar, Manisha, Malphettes, Marion, Martelius, Timi, Mehta, Mehek, Metcalfe, Dean D., Meyts, Isabelle, Nannapaneni, Naveen, Ocejo Vinyals, J. Gonzalo, Olivier, Kenneth, Ombrello, Amanda K., Orange, Jordan S., Rabinovitch, Nathan, Rauscher, Christine K., Redfern, Ann, Reynolds, Paul R., Rieux-Laucat, Frederic, Secord, Elizabeth, Seeborg, Filiz O., Seppänen, Mikko R.J., Sereti, Irini, Shin, Daniel S., Shin, Junghee J., Snapper, Scott B., Suri, Deepti, Tangcheewinsirikul, Sirikarn, Thatayatikom, Akaluck, Torgerson, Troy, Touzot, Fabien, Uzel, Gulbu, Varjosalo, Markku, Vasconcelos, Dewton F.P., von Bernuth, Horst, Walsh, Thomas, Walter, Jolan E., Ward, Brant R., Wittkowski, Helmut, Wysocki, Christian A., Baysac, Kathleen, Sun, Guangping, Nakano, Hiroto, Schmitz, Elizabeth G., Cruz, Anthony C., Fisher, Charles, Bailey, Alexis C., Mace, Emily, Milner, Joshua D., and Ombrello, Michael J.

Published
2024
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24. Determining minimal clinically important difference estimates following surgery for degenerative conditions of the lumbar spine: analysis of the Canadian Spine Outcomes and Research Network (CSORN) registry

Author

Power, J. Denise, Perruccio, Anthony V., Canizares, Mayilee, McIntosh, Greg, Abraham, Edward, Attabib, Najmedden, Bailey, Christopher S., Charest-Morin, Raphaële, Dea, Nicholas, Finkelstein, Joel, Fisher, Charles, Glennie, R. Andrew, Hall, Hamilton, Johnson, Michael G., Kelly, Adrienne M., Kingwell, Stephen, Manson, Neil, Nataraj, Andrew, Paquet, Jérôme, Singh, Supriya, Soroceanu, Alex, Thomas, Kenneth C, Weber, Michael H., and Rampersaud, Y. Raja

Published
2023
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29. Patient's expectations of surgery for lumbar degenerative spondylolisthesis: analysis by type of surgery and patient factors from the Canadian Spine Outcomes and Research Network (CSORN)

Author

Dandurand, Charlotte, Urquhart, Jennifer, Bailey, Christopher S., Ailon, Tamir, Charest-Morin, Raphaele, Dea, Nicolas, Dvorak, Marcel, Glennie, Andrew, Kwon, Brian K., Paquette, Scott, Rampersaud, Y. Raja, Street, John T., Hounjet, Celine, Mashayekhi, Mohammad Sadegh, and Fisher, Charles G.

Published
2023
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32. Patient-Reported and Clinical Outcomes of Surgically Treated Patients With Symptomatic Spinal Metastases: Results From Epidemiology, Process, and Outcomes of Spine Oncology (EPOSO), a Prospective, Multi-Institutional and International Study

Author

Barzilai, Ori, primary, Sahgal, Arjun, additional, Rhines, Laurence D., additional, Versteeg, Anne L., additional, Sciubba, Daniel M., additional, Lazary, Aron, additional, Weber, Michael H., additional, Schuster, James M., additional, Boriani, Stefano, additional, Bettegowda, Chetan, additional, Arnold, Paul M., additional, Clarke, Michelle J., additional, Laufer, Ilya, additional, Fehlings, Michael G., additional, Gokaslan, Ziya L., additional, and Fisher, Charles G., additional

Published
2024
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35. Pre-operative expectations of patients with Degenerative Cervical Myelopathy: An observational study from the Canadian Spine Outcomes and Research Network

Author

Althagafi, Alwalaa, primary, Dea, Nicolas, additional, Evaniew, Nathan, additional, Rampersaud, Raja Y, additional, Jacobs, W. Bradley, additional, Paquet, Jérome, additional, Wilson, Jefferson R, additional, Hall, Hamilton, additional, Bailey, Christopher S., additional, Weber, Michael H., additional, Nataraj, Andrew, additional, Attabib, Najmedden, additional, Cadotte, David W., additional, Phan, Philippe, additional, Christie, Sean D., additional, Fisher, Charles G., additional, Manson, Neil, additional, Thomas, Kenneth, additional, McIntosh, Greg, additional, and Charest-Morin, Raphaële, additional

Published
2024
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36. Factors associated with increased length of stay in degenerative cervical spine surgery: a cohort analysis from the Canadian Spine Outcomes and Research Network

Author

Moskven, Eryck, primary, McIntosh, Greg, additional, Nataraj, Andrew, additional, Christie, Sean D., additional, Kumar, Rajesh, additional, Phan, Philippe, additional, Wang, Zhi, additional, Tarabay, Bilal, additional, Weber, Michael H., additional, Singh, Supriya, additional, Bailey, Christopher S., additional, Manson, Neil A., additional, Abraham, Edward, additional, Paquet, Jérôme, additional, Wilson, Jefferson R., additional, Rampersaud, Y. Raja, additional, Fisher, Charles G., additional, Dea, Nicolas, additional, and Charest-Morin, Raphaële, additional

Published
2024
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39. Outcome of spine surgery in patients with depressed mental states: a Canadian spine outcome research network study

Author

Cushnie, Duncan, Soroceanu, Alex, Stratton, Alexandra, Dea, Nicolas, Finkelstein, Joel, Bailey, Christopher S., Weber, Michael H., Paquet, Jerome, Glennie, Andrew, Hall, Hamilton, Rampersaud, Raja, Ahn, Henry, Kelly, Adrienne, Christie, Sean, Nataraj, Andrew, Johnson, Michael, Abraham, Ed, Attabib, Najmedden, Fisher, Charles, Manson, Neil, and Thomas, Kenneth

Published
2022
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42. Differentiation of pain-related functional limitations in surgical patients with lumbar spinal stenosis (LSS) using the Oswestry Disability Index: a Canadian Spine Outcomes and Research Network (CSORN) study

Author

Charalampidis, Anastasios, Canizares, Mayilee, Kalsi, Pratipal S., Hung Wu, Pang, Johnson, Michael, Soroceanu, Alex, Nataraj, Andrew, Glennie, Andrew, Rasoulinejad, Parham, Attabib, Najmedden, Hall, Hamilton, Fisher, Charles, Thomas, Kenneth, and Rampersaud, Yoga Raja

Published
2022
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44. Postoperative recovery patterns following discectomy surgery in patients with lumbar radiculopathy

Author

Wang, Shuaijin, Hebert, Jeffrey J., Abraham, Edward, Vandewint, Amanda, Bigney, Erin, Richardson, Eden, El-Mughayyar, Dana, Attabib, Najmedden, Wedderkopp, Niels, Kingwell, Stephen, Soroceanu, Alex, Weber, M. H., Hall, Hamilton, Finkelstein, Joel, Bailey, Christopher S., Thomas, Kenneth, Nataraj, Andrew, Paquet, Jerome, Johnson, Michael G., Fisher, Charles, Rampersaud, Y. Raja, Dea, Nicolas, Small, Chris, and Manson, Neil

Published
2022
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48. The Effects of Peri-Operative Adverse Events on Clinical and Patient-Reported Outcomes After Surgery for Degenerative Cervical Myelopathy: An Observational Cohort Study from the Canadian Spine Outcomes and Research Network.

Author

Malhotra, Armaan K., Evaniew, Nathan, Dea, Nicolas, Fisher, Charles G., Street, John T., Cadotte, David W., Jacobs, W. Bradley, Thomas, Kenneth C., Attabib, Najmedden, Manson, Neil, Hall, Hamilton, Bailey, Christopher S., Nataraj, Andrew, Phan, Philippe, Raja Rampersaud, Y., Paquet, Jerome, Weber, Michael H., Christie, Sean D., McIntosh, Greg, and Wilson, Jefferson R.

Published
2024
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49. Increasing acceptance of AI‐generated digital twins through clinical trial applications.

Author

Vidovszky, Anna A., Fisher, Charles K., Loukianov, Anton D., Smith, Aaron M., Tramel, Eric W., Walsh, Jonathan R., and Ross, Jessica L.

Subjects
*DIGITAL twins, *CLINICAL trials, *CLINICAL medicine, *TECHNOLOGICAL innovations, *INDIVIDUALIZED medicine
Abstract

Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real‐world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI‐generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI‐generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI‐generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI‐generated digital twins in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Anterior vs Posterior Surgery for Patients With Degenerative Cervical Myelopathy: An Observational Study From the Canadian Spine Outcomes and Research Network.

Author

Evaniew, Nathan, Bailey, Christopher S., Rampersaud, Y. Raja, Jacobs, W. Bradley, Phan, Philippe, Nataraj, Andrew, Cadotte, David W., Weber, Michael H., Thomas, Kenneth C., Manson, Neil, Attabib, Najmedden, Paquet, Jerome, Christie, Sean D., Wilson, Jefferson R., Hall, Hamilton, Fisher, Charles G., McIntosh, Greg, and Dea, Nicolas

Published
2024
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