Patient-specific Scores Research Articles

A Vulnerability Index to Assess the Risk of SARS-CoV-2-Related Hospitalization/Death: Urgent Need for an Update after Diffusion of Anti-COVID Vaccines.

Background: There are algorithms to predict the risk of SARS-CoV-2-related complications. Given the spread of anti-COVID vaccination, which sensibly modified the burden of risk of the infection, these tools need to be re-calibrated. Therefore, we updated our vulnerability index, namely, the Health Search (HS)-CoVulnerabiltyIndex (VI)d (HS-CoVId), to predict the risk of SARS-CoV-2-related hospitalization/death in the primary care setting. Methods: We formed a cohort of individuals aged ≥15 years and diagnosed with COVID-19 between 1 January and 31 December 2021 in the HSD. The date of COVID-19 diagnosis was the study index date. These patients were eligible if they had received an anti-COVID vaccine at least 15 days before the index date. Patients were followed up from the index date until one of the following events, whichever came first: COVID-19-related hospitalization/death (event date), end of registration with their GPs, and end of the study period (31 December 2022). To calculate the incidence rate of COVID-19-related hospitalization/death, a patient-specific score was derived through linear combination of the coefficients stemming from a multivariate Cox regression model. Its prediction performance was evaluated by obtaining explained variation, discrimination, and calibration measures. Results: We identified 2192 patients who had received an anti-COVID vaccine from 1 January to 31 December 2021. With this cohort, we re-calibrated the HS-CoVId by calculating optimism-corrected pseudo-R2, AUC, and calibration slope. The final model reported a good predictive performance by explaining 58% (95% CI: 48-71%) of variation in the occurrence of hospitalizations/deaths, the AUC was 83 (95% CI: 77-93%), and the calibration slope did not reject the equivalence hypothesis (p-value = 0.904). Conclusions: Two versions of HS-CoVId need to be differentially adopted to assess the risk of COVID-19-related complications among vaccinated and unvaccinated subjects. Therefore, this functionality should be operationalized in related patient- and population-based informatic tools intended for general practitioners.

99mTc-HMPAO SPECT Perfusion Signatures Associated With Clinical Progression in Patients With Isolated REM Sleep Behavior Disorder.

Idiopathic/isolated REM sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies and Parkinson disease. Despite evidence of abnormal cerebral perfusion in iRBD, there is currently no pattern that can predict whether an individual will develop dementia with Lewy bodies or Parkinson disease. The objective was to identify a perfusion signature associated with conversion to dementia with Lewy bodies in iRBD. Patients with iRBD underwent video-polysomnography, neurologic and neuropsychological assessments, and baseline 99mTc-HMPAO SPECT to assess relative cerebral blood flow. Partial least squares correlation was used to identify latent variables that maximized covariance between 27 clinical features and relative gray matter perfusion. Patient-specific scores on the latent variables were used to test the association with conversion to dementia with Lewy bodies compared with that with Parkinson disease. The signature's expression was also assessed in 24 patients with iRBD who underwent a second perfusion scan, 22 healthy controls, and 19 individuals with Parkinson disease. Of the 137 participants, 93 underwent SPECT processing, namely 52 patients with iRBD (67.9 years, 73% men), 19 patients with Parkinson disease (67.3 years, 37% men), and 22 controls (67.0 years, 73% men). Of the 47 patients with iRBD followed up longitudinally (4.5 years), 12 (26%) developed a manifest synucleinopathy (4 dementia with Lewy bodies and 8 Parkinson disease). Analysis revealed 2 latent variables between relative blood flow and clinical features: the first was associated with a broad set of features that included motor, cognitive, and perceptual variables, age, and sex; the second was mostly associated with cognitive features and RBD duration. When brought back into the patient's space, the expression of the first variable was associated with conversion to a manifest synucleinopathy, whereas the second was associated with conversion to dementia with Lewy bodies. The expression of the patterns changed over time and was associated with worse motor features. This study identified a brain perfusion signature associated with cognitive impairment in iRBD and transition to dementia with Lewy bodies. This signature, which can be derived from individual scans, has the potential to be developed into a biomarker that predicts dementia with Lewy bodies in at-risk individuals.

Genetic variant annotation scores in congenital long QT syndrome.

Congenital Long QT Syndrome (LQTS) is a hereditary arrhythmic disorder. We aimed to assess the performance of current genetic variant annotation scores among LQTS patients and their predictive impact. We evaluated 2025 patients with unique mutations for LQT1-LQT3. A patient-specific score was calculated for each of four established genetic variant annotation algorithms: CADD, SIFT, REVEL, and PolyPhen-2. The scores were tested for the identification of LQTS and their predictive performance for cardiac events (CE) and life-threatening events (LTE) and then compared with the predictive performance of LQTS categorization based on mutation location/function. Score performance was tested using Harrell's C-index. A total of 917 subjects were classified as LQT1, 838 as LQT2, and 270 as LQT3. The identification of a pathogenic variant occurred in 99% with CADD, 92% with SIFT, 100% with REVEL, and 86% with PolyPhen-2. However, none of the genetic scores correlated with the risk of CE (Harrell's C-index: CADD = 0.50, SIFT = 0.51, REVEL = 0.50, and PolyPhen-2 = 0.52) or LTE (Harrell's C-index: CADD = 0.50, SIFT = 0.53, REVEL = 0.54, and PolyPhen-2 = 0.52). In contrast, high-risk mutation categorization based on location/function was a powerful independent predictor of CE (HR = 1.88; p < .001) and LTE (HR = 1.89, p < .001). In congenital LQTS patients, well-established algorithms (CADD, SIFT, REVEL, and PolyPhen-2) were able to identify the majority of the causal variants as pathogenic. However, the scores did not predict clinical outcomes. These results indicate that mutation location/functional assays are essential for accurate interpretation of the risk associated with LQTS mutations.

Abstract 5472: Utilizing response in immune checkpoint inhibitor treated cohorts improves clinical applicability of neoantigen immunogenicity predictions

Abstract Introduction: Neoantigen-based biomarkers have improved predictions of response to immune checkpoint blockade (ICB) therapy, highlighting the importance of accurate prediction of immunogenic neoantigen candidates. A challenge in developing robust immunogenicity prediction models is limited availability of sequencing-associated immunogenicity data for evaluating methods due to the complexity of generating such datasets. We propose a novel approach to optimize prediction models of immunogenic neoantigens using a meta-analysis framework based on multiple ICB cohorts. Methods: To build on the 110 mono-allelic immunopeptidomics-derived SHERPA® MHC binding prediction framework, we engineered T-cell recognition features on two datasets: peptide-centric data aggregated by Schmidt et al. and patient-specific exome and transcriptome sequencing data from the TESLA consortium. We developed two-tiered models based on the feature landscapes of both datasets to predict peptide-MHC (pMHC) immunogenicity, incorporating features with significant performance gains. We systematically re-processed publicly available DNA and RNA sequencing data from over 500 ICB treated patients spanning 12 different cohorts across five different cancer types with a harmonized bioinformatics pipeline. We then evaluated the performance of each model consisting of a unique combination of immunogenic features across the ICB training (N=7) and validation (N=5) cohorts using a meta-analysis framework. Results: We evaluated iterations of SHERPA-Immunogenicity (SI) models using the Schmidt et al. and TESLA datasets, resulting in a range of performance metrics (area under the precision recall curves of 0.74-0.84 and positive predictive values of 0.32-0.54). After aggregating pMHC predictions into patient-specific scores based on the most immunogenic peptide present (SHERPA-Immunogenicity Maximum - SIM) or the quantity of immunogenic peptides identified (SHERPA-Immunogenicity Burden - SIB), we observed that responders had higher SIM and SIB scores compared to non-responders across the melanoma training cohorts. We found SIM scores outperformed SIB scores, suggesting the degree of epitope immunogenicity may be a critical factor in predicting response. The model with the most significant meta p-value for ICB response in melanoma cohorts (OR=2.43, p=0.006) also predicted overall survival in 3/5 melanoma cohorts (p&amp;lt0.05). Conclusions: We developed a novel framework to predict neoantigen immunogenicity utilizing meta-analysis of ICB cohorts to overcome dataset limitations and gain prediction performance confidence. We look forward to supporting personalized cancer vaccine development with our pMHC immunogenicity predictions and applying our predictive biomarker on additional ICB cohorts. Citation Format: Hima Anbunathan, Neeraja Ravi, Rachel Marty Pyke, Steven Dea, Richard O. Chen, Sean Michael Boyle. Utilizing response in immune checkpoint inhibitor treated cohorts improves clinical applicability of neoantigen immunogenicity predictions. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5472.

Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments.

In this work, we propose a method for individualized treatment selection when there are correlated multiple responses for the treatment ( ) scenario. Here we use ranks of quantiles of outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables using any number of quantiles and it can be applied for a broad set of models. We propose a rank aggregation technique for combining several lists of ranks where both these lists and elements within each list can be correlated. The method has the flexibility to incorporate patient and clinician preferences into the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present illustrations using two different datasets from diabetes and HIV-1 clinical trials to show the applicability of the proposed procedure for real data.

Optimal personalized treatment selection with multivariate outcome measures in a multiple treatment case

In this work we propose a novel method for individualized treatment selection when there are correlated multiple treatment responses. For the K treatment ( ) scenario, we compare quantities that are suitable indexes based on outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique that takes into account possible correlations among ranked lists to estimate an ordering of treatments based on treatment performance measures such as the smooth conditional mean. The method has the flexibility to incorporate patient and clinician preferences into the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present data analyses using HIV clinical trial data to show the applicability of the proposed procedure for real data.

PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

BackgroundRecent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources.ResultsWe propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters.ConclusionsOur suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.

Selection of the optimal personalized treatment from multiple treatments with multivariate outcome measures

ABSTRACTIn this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. For the K treatment (K ≥2) scenario we compare quantities that are suitable indexes based on outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to estimate an ordering of treatments based on ranked lists of treatment performance measures such as smooth conditional means and conditional probability of a response for one treatment dominating others. The method has the flexibility to incorporate patient and clinician preferences to the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present data analyses using HIV and Diabetes clinical trials data to show the applicability of the proposed procedure for real data.

Personalized treatment selection using data from crossover designs with carry-over effects.

In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry-over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry-over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient-specific scores constructed from patient covariates. We suggest single-index models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a single-index model structure. We also conduct a real data analysis to show the applicability of the proposed procedure.

A probability based method for selecting the optimal personalized treatment from multiple treatments.

In this work we propose a method for optimal treatment assignment based on individual covariate information for a patient. For the K treatment ( ) scenario, we compare quantities that are suitable surrogates to true conditional probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient specific scores constructed from patient-specific covariates. As opposed to methods based on conditional means, our method can be applied for a broad set of models and error structures. Furthermore, the proposed method has very desirable large sample properties. We suggest Single Index Models as appropriate models connecting outcome variables to covariates and our empirical investigations show that correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a Single Index Model structure. Furthermore, selection of a treatment using the proposed metric appears to incur no losses in terms of the average reward for cases when two treatments are close in terms of this metric. We also conduct a real data analysis to show the applicability of the proposed procedure. This analysis highlights possible gains both in terms of average response and survival time if one were to use the proposed method.

Osteoarthrosis knee: Need for a simplified prognostic knee score

Osteoarthrosis is the commonest joint disorder. There may be variations depending upon the type of joint involved and amount of loading of joint. Weight bearing joints appears to have a higher predisposition as compared to other joints. The treatment modalities for symptom relief vary from nonpharmacological to pharmacological and surgical interventions. The type of intervention required depends mainly on the amount of joint destruction which can be quantified clinically and radiologically. Knee joint is one of most common joints affected in destructive pathology and taken for the most invasive procedure i.e joint replacement. Treatment choice is based on clinical evaluation,radiological changes and patient specific scores. Osteoarthritis of knee can be primary or secondary and symptoms complex can be quantified using various scales like WOMAC, HSS, KOOS, OKS and KSS. The radiological criteria of Kellgren and Lawrence can be applied to assess the extent of destruction. The two extremes of all criteria explains the normal and worst conditions. No criteria explains the amount or the type of intervention needed for a particular stage of disease. All criteria being nonuniform and diverse. The other most important thing being difficult utility and tredious scoring system. The present need is a single, easy,clinically validated scoring system which combines clinical,radiological and patient reported variables which can guide treatment interventions and has the ability to prognosticate the disease. New score (KALIA AND RAINA Score) can be recognised as a new assessment tool in evaluating a patient for osteoarthrosis knee in future.

Ask patients what they want. Evaluation of individual complaints before total hip replacement

We aimed to assess individual differences in complaints in patients just before total hip replacement (THR) and the importance attached to the relief of each of them. In a pilot study, using open-ended interviews, we identified 16 main complaints, four of which (night pain, unequal leg length and discomfort during sexual and recreational activities) were not included in any of the six hip-rating scales in general use. Each of the 16 complaints was then assessed in 72 patients and rated for severity and the relative importance of relief. From this we calculated a severity-importance rating for each complaint and a patient-specific score for all complaints. The 72 patients had a mean age of 64 years (17 to 92) and 51% were men. The most important reasons for wanting a THR were day pain and walking difficulty, but the complaints mentioned above and not included in standard hip scores were also important. Greater attention to the individual requirements of patients might improve evaluation of the outcome of orthopaedic treatments.