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Machine Learning Prediction of Treatment Outcome in Late-Life Depression

ABSTRACT: Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.

SUBMITTER: Grzenda A

PROVIDER: S-EPMC8563624 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveCurrently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features.MethodsLate-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models.ResultsA learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity.ConclusionsCombinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment.

Project description:BACKGROUND:Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. METHODS AND FINDINGS:This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined. CONCLUSION:This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.

Project description:OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

Project description:ImportanceMany older adults with depression do not experience remission with antidepressant treatment, and markers of cellular senescence in late-life depression (LLD) are associated with greater severity of depression, greater executive dysfunction, and higher medical illness burden. Since these clinical characteristics are associated with remission in LLD, molecular and cellular senescence abnormalities could be a possible biological mechanism underlying poor treatment response in this population.ObjectiveTo examine whether the senescence-associated secretory phenotype (SASP) index was associated with the likelihood of remission from a depressive episode in older adults.Design, setting, and participantsA nonrandomized, open-label clinical trial was conducted between August 2009 and August 2014 in Pittsburgh, Pennsylvania; St Louis, Missouri; and Toronto, Ontario, Canada, with older adults in a current major depressive episode according to the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision) diagnostic criteria. Data from biomarker analyses were reported according to the clinical trial archived plasma samples run in March 2021. Data were analyzed from June to November 2021.ExposureVenlafaxine extended release (dose ranging from 37.5 mg to 300 mg daily) for up to 12 weeks.Main outcomes and measuresThe association between a composite biomarker-based index (SASP index) and treatment remission in older adults with major depression was measured using clinical data and blood samples.ResultsThere were 416 participants with a mean (SD) age of 60.02 (7.13) years; 64% (265 participants) were self-reported female, and the mean (SD) Montgomery-Asberg Depression Rating Scale score was 26.6 (5.7). Higher SASP index scores were independently associated with higher rates of nonremission, with an increase of 1 unit in the SASP index score increasing the odds of nonremission by 19% (adjusted odds ratio, 1.19; 95% CI, 1.05-1.35; P = .006). In contrast, no individual SASP factors were associated with remission in LLD.Conclusions and relevanceUsing clinical data and blood samples from a nonrandomized clinical trial, the results of this study suggest that molecular and cellular senescence, as measured with the SASP index, is associated with worse treatment outcomes in LLD. Combining this index score reflecting interrelated biological processes with other molecular, clinical, and neuroimaging markers may be useful in evaluating antidepressant treatment outcomes. These findings inform a path forward for geroscience-guided interventions targeting senescence to improve remission rates in LLD.Trial registrationClinicalTrials.gov Identifier: NCT00892047.

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Machine Learning Prediction of Treatment Outcome in Late-Life Depression

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