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Purpose This scoping review aims to identify and summarise artificial intelligence (AI) methods applied to patient-reported outcome measures (PROMs) for prediction of patient outcomes, such as survival, quality of life, or treatment decisions. Introduction AI models have been successfully applied to predict outcomes for patients using mainly clinically focused data. However, systematic guidance for utilising AI and PROMs for patient outcome predictions is lacking. This leads to inconsistency of model development and evaluation, limited practical implications, and poor translation to clinical practice. Materials and methods This review was conducted across Web of Science, IEEE Xplore, ACM, Digital Library, Cochrane Central Register of Controlled Trials, Medline and Embase databases. Adapted search terms identified published research using AI models with patient-reported data for outcome predictions. Papers using PROMs data as input variables in AI models for prediction of patient outcomes were included. Results Three thousand and seventy-seven records were screened, 94 of which were included in the analysis. AI models applied to PROMs data for outcome predictions are most commonly used in orthopaedics and oncology. Poor reporting of model hyperparameters and inconsistent techniques of handling class imbalance and missingness in data were found. The absence of external model validation, participants’ ethnicity information and stakeholders involvement was common. Conclusion The results highlight inconsistencies in conducting and reporting of AI research involving PROMs in patients’ outcomes predictions, which reduces the reproducibility of the studies. Recommendations for external validation and stakeholders’ involvement are given to increase the opportunities for applying AI models in clinical practice.

Background Continuous Quality of Life (QoL) monitoring enables many benefits, such as early healthcare interventions. This work uses Internet of Health Things (IoHT) data and Machine Learning to infer physical and psychological Quality of Life measures. Methods We conducted a longitudinal study with 44 participants for six months. Health data were collected daily through smartphones and wearables, and the participants answered the WHOQOL-BREF questionnaire weekly. Then, five Machine Learning models were trained to evaluate their ability to estimate users’ QoL. Results Random Forest (RF) had the best results considering the Root Mean Squared Error (RMSE). RF got an RMSE of 7.8618 for the physical domain and 7.4591 for the psychological domain. Conclusions Overall, it is possible to use IoHT data to infer users’ QoL, considering a certain margin of error; RF had a reasonable performance for this problem and it was not found any decisive feature for the inference process. This last point reinforces that QoL inference using IoHT data is not trivial, and only combining a large number of features can give relevant insights into users’ QoL. Project approved by UFC ethics committee (ID 56153322.0.0000.5054) on March 9, 2022.

Background Utero-vaginal brachytherapy (BT) is an irreplaceable care component for the curative treatment of locally advanced cervix cancer (LACC). Magnetic Resonance Imaging (MRI)-image guided adaptive BT (IGABT) using the GYN-GEC-ESTRO EMBRACE guidelines is the international care standard. Usually following chemo–radiation therapy (CRT), IGABT has high proven utility in LACC but requires significant health system resources. Timely access was disrupted by the COVID-19 pandemic which challenged us to re-design our established IGABT care pathway. Methods From April 2020 consecutive patients with LACC were enrolled after CRT in a single arm exploratory non-inferiority study of a modified IGABT (mIGABT) protocol. This delivered an iso-effective IGABT dose (39.3 Gy: EQD2: α/β10Gy concept) over a 24-h period during a single overnight hospitalisation. Results Fourteen LACC patients received mIGABT from April 2020 to March 2022. Median age was 62.5 years (37–82 years). LACC histology was primary squamous (9/14) or adeno-carcinoma (5/14). International Federation of Gynaecology and Obstetrics (FIGO) 2018 stages ranged from IB1/2 (N = 3), IIA1/IIB (5), IIIB (2), IIIC1/2 (4) with mean ± standard deviation (SD) gross tumour volume-at-diagnosis (GTV_D) of 37.7 cc ± 71.6 cc. All patients achieved complete metabolic, clinical, and cytologic cancer response with CRT and IGABT. High-risk HPV was cleared by 6-months. Complete MRI-defined cancer response before mIGABT (GTV_Fx1) was seen in 77% of cases (10/13). Only two women developed metastatic disease and one died at 12-months; 13 patients were alive without cancer at mean 20.3 ± 7.2 months follow-up. Actuarial 2-year overall survival was 93%. Compared with our pre-COVID IGABT program, overall mIGABT cost-saving in this cohort was USD 22,866. Prescribed dose covered at least 90% (D90) of the entire cervix and any residual cancer at time of BT (HRCTV_D90: high-risk clinical target volume) with 3-fractions of 8.5 Gy delivered over 24-h (22.8 ± 1.7 h). Total treatment time including CRT was 38 days. The mIGABT schedule was well tolerated and the entire cohort met EMBRACE recommended (EQD2: α/β10Gy) combined HRCTV_D90 coverage of 87.5 ± 3.7 Gy. Similarly, organ-at-risk (OAR) median: interquartile range D2cc constraints (EQD2: α/β3Gy) were EMBRACE compliant: bladder (65.9 Gy: 58.4–72.5 Gy), rectum (59.1 Gy: 55.7–61.8 Gy), and sigmoid colon (54.6 Gy: 50.3–58.9 Gy). ICRU recto-vaginal point dose was significantly higher (75.7 Gy) in our only case of severe (G4) pelvic toxicity. Conclusions This study demonstrated the utility of mIGABT and VMAT CRT in a small cohort with LACC. Loco-regional control was achieved in all cases with minimal emergent toxicity. Single insertion mIGABT was logistically efficient, cost-saving, and patient-centric during the COVID-19 pandemic.

Objective With survival steadily improving among people living with HIV(PLWH), quality of life (QoL) has emerged as the ultimate benchmark of therapeutic success. We therefore aimed to develop and validate machine learning models that predict QoL trend in PLWH, identifying key determinants to inform personalized interventions and optimize long-term well-being. Methods In this longitudinal observational study, PLWH were recruited from March 2024 to December 2024. Sociodemographic and clinical variables were collected, and the 31-item WHOQOL-HIV BREF was adopted as the QoL measure. The symptom experience was assessed using the Self-Report Symptom Scale (SRSS). All variables were incorporated into machine learning models to develop predictive algorithms. Results This study included 676 eligible participants with HIV in the cohort. The Gaussian Process (GP) model demonstrated the highest testing AUC of 0.811 and 0.815 in the training dataset. The GP model excels in metrics such as accuracy, AUC, recall, precision, F1 score, Kappa, MCC, Log Loss, and Brier score. In the decision curve analysis (DCA), the five machine learning models exhibited similar net benefits over a broad range of threshold probabilities (ranging from 0.2 to 0.8) compared to the Random Forest (RF) model. The GP and the MLP showed enhanced net benefits at intermediate to high threshold probabilities (30 ~ 60%). The SHAP technique identified the top four predictors of QoL, ranked by importance, with symptom burden being highlighted as the most critical predictor variable. Conclusions The machine-learning model, predominantly a GP model, demonstrated the better predictive performance among the six models evaluated, for detecting the QoL predictor in PLWH, indicating that symptom burden influences QoL level. Our findings highlight a non-linear relationship between ART duration and QoL, with diminished well-being during mid-treatment (6 ~ 10 years) linked to treatment fatigue and cumulative toxicities, emphasizing the necessity of dynamic psychosocial support and tailored interventions to sustain long-term QoL in HIV care.

Background Arthritis is a prevalent chronic disease substantially impacting patients’ quality of life (QoL). While identifying key determinants associated with arthritis is critical for targeted interventions, traditional statistical methods often struggle with complex interactions, and existing machine learning (ML) approaches frequently lack the interpretability needed to guide clinical decisions. This study integrates a comprehensive, explainable machine learning (XAI) workflow to identify and interpret key QoL-related predictors of arthritis status in a large national cohort. Methods Data were obtained from 15,011 participants aged > 45 years in the 2020 China Health and Retirement Longitudinal Study (CHARLS). We initially selected 55 potential QoL-related predictors spanning demographic, functional, pain, psychosocial, and lifestyle domains. Feature engineering was performed to create aggregate scores, indicators, and binned variables. Missing data were handled using imputation combined with missing indicator variables. A LightGBM-based feature selection process identified 68 key predictors. Nine ML models (including Logistic Regression, RandomForest, GradientBoosting, LightGBM, CatBoost, XGBoost, DecisionTree, NaiveBayes, KNN) were developed using SMOTE-resampled training data, with hyperparameters optimized via Optuna targeting recall. Performance was evaluated on a held-out test set using Area Under the ROC Curve (AUC), Average Precision (AP), Recall, Specificity, Precison, and F1-score. SHapley Additive exPlanations (SHAP) analysis was applied to the best-performing model (GradientBoosting) for interpretation. Results Several models achieved strong predictive performance, with GradientBoosting yielding the highest AUC (0.767, 95% CI: 0.752–0.782) and high AP (0.678, 95% CI: 0.655–0.702). SHAP analysis identified multi-site pain burden (particularly knee/leg pain and pain location count), age, self-rated health, sleep quality, functional limitations (ADL counts/scores), and negative affect as the most influential predictors driving arthritis status prediction. Conclusions This study successfully applied an XAI pipeline to identify and rank key QoL-related factors predictive of arthritis status in a large Chinese cohort, achieving robust model performance. Pain burden, age, subjective health, sleep, functional status, and psychological well-being are critical determinants. These interpretable findings can inform risk stratification and guide targeted interventions focusing on these key areas to potentially improve arthritis management.

Background Existing research has identified health‑related quality of life (HRQoL) is influenced by a multitude of factors among children and adolescents. However, there has been relatively limited exploration of the multidimensional predictive factors (individual characteristics, health risk behaviors, and negative life events) that contribute to HRQoL. This study aimed to develop a nomogram to predict the HRQoL in children and adolescents. Methods A total of 12,145 children and adolescents were surveyed using stratified cluster sampling method, randomly divided into a training set ( n  = 8503) and a validation set ( n  = 3642). Logistic regression, lasso regression, and random forest models were combined to identify the most significant predictors of HRQoL. A nomogram was constructed using multivariate logistic regression. The receiver operating characteristic curve, k -fold cross-validation, decision curve analysis (DCA), and internal validation were used to assess the accuracy, discrimination, and generalization of the nomogram. Results Non-suicidal self-injury, academic burnout, parental abuse, stress, bullying victimization, healthy diet, and sleep were found to be significant predictors of HRQoL. The area under the curve (AUC) of the training set was 0.765, whereas that of the validation data was 0.775. The k -fold cross-validation ( k  = 10) revealed good discrimination in internal validation (mean AUC = 0.771). The nomogram had good clinical use since the DCA covered a large threshold probability: 5%-89% (in the training set) and 4%-81% (in the validation set). Conclusions The nomogram prediction model constructed in this study can provide a reference for predicting HRQoL in children and adolescents.

Background Parkinson’s disease (PD) considerably impacts health-related quality of life (HRQoL) through motor and non-motor symptoms. The Parkinson’s Disease Questionnaire-39 (PDQ-39) is the most widely used tool to assess HRQoL, encompassing eight dimensions and a Summary Index providing an overall score. Despite advances in machine learning (ML) for predicting disease symptoms and progression, its application to predict HRQoL across these dimensions remains underexplored. Methods This study uses complete-case data for 478 of 861 patients from PRISM, a cross-sectional observational survey conducted in six European countries in 2018–2019. Participants were adults with PD recruited through advocacy groups and clinical centers who completed online assessments, providing data on demographics, medication, comorbidities, and disease characteristics (Tolosa et al., 2021). ML models were trained to predict PDQ-39 dimensions and Summary Index scores (0–100; higher = worse HRQoL). Features were preselected using the Boruta algorithm on the training data. Model selection was based on the lowest mean RMSE from 100 bootstrap resamples on the training set. Selected models were then retrained using 1000 bootstrap resamples for robust performance estimation. Final performance was evaluated on a held-out 20% validation set using R², MAE, and RMSE. Feature importance was assessed using permutation importance with MAE loss (100 permutations) on the held-out validation set. Factor Analysis of Mixed Data (FAMD) was used to explore patterns between non-motor symptoms and PDQ-39. Results Selected models: xgbTree (Summary Index; Activities of Daily Living) and gaussprPoly (all other PDQ-39 dimensions). On the validation set, Summary Index/ Cognitions showed the strongest performance with R² = 0.56/0.53, MAE = 9.60/12.39, RMSE = 12.66/16.20. Permutation feature importance ranked the Non-Motor Symptoms Questionnaire score (sum of 30 non-motor symptoms, range 0–30) as the most important predictor across all models. FAMD showed clustering of Social Support, Bodily Discomfort, and Stigma dimensions with Anxiety. Conclusions Our findings demonstrate the critical role of non-motor symptoms in predicting HRQoL in patients with PD. While ML models effectively predict overall HRQoL and cognitive aspects, achieving comparable performance on other dimensions may require additional variables to reduce error. These insights emphasize comprehensive treatment strategies addressing both motor and non-motor symptoms.

Lymphangioleiomyomatosis (LAM) is a rare, female-specific cystic lung disease in which destruction of the lung parenchyma is driven by lesions containing TSC2-/- LAM cells and recruited stromal LAM associated fibroblasts (LAFs). LAM patients can be treated with rapamycin to stabilise lung function, but some patients continue to decline, and additional therapies are needed for these patients. We hypothesised that extracellular matrix (ECM) deposited by LAFs within lesions could affect LAM cell behaviour and promote disease progression. We aimed to quantify ECM deposition in LAM, investigate its association with disease severity and study the effect of LAF deposited matrix on LAM cell behaviour in vitro. MethodsCollagen neoepitopes were measured in sera from 96 LAM patients and 22 controls using Nordic Bioscience assays. Collagen deposition in paraffin sections of LAM lung tissue was assayed by picrosirius red (PSR) staining and immunohistochemistry from 19 lung samples with linked clinical data. In vitro assays were performed on TSC2-/- cells grown on cell-free LAF-derived ECM.ResultsSerum markers of collagen, but not elastin turnover tended to be greater in women with LAM than controls (C6M, p=0.057). Quantification of PSR staining revealed trends toward ECM accumulation with increasing disease duration (r=0.62, p=0.060) and reducing DLCO (r=-0.55, p=0.057). LAF-derived ECM increased proliferation (p<0.0001) and reduced the anti-proliferative effect of rapamycin in TSC2-/- cells (p=0.0004) in vitro.ConclusionCollagen deposition can be observed in LAM lesions. LAF-derived ECM enhances TSC2-/- cell proliferation in vitro and may contribute to disease progression by providing a pro-proliferative microenvironment for LAM cells in vivo. This may reduce response to rapamycin in some patients. These data support the investigation of anti-fibrotic therapies for LAM patients who respond poorly to rapamycin.

IntroductionPulmonary veno-occlusive disease (PVOD) is an incurable condition characterised by the progressive remodelling and narrowing of small pulmonary veins, venules and capillaries. This leads to right ventricular hypertrophy, pulmonary hypertension, and death within 1–2 years if untreated. In 2014, homozygous mutations in the stress sensing kinase GCN2 were shown to be the main genetic cause of PVOD. GCN2 is activated by amino acid starvation and phosphorylates the alpha subunit of eIF2 which reduces global translation while increasing the translation of cytoprotective transcripts such as activating transcription factor 4.Methods and ResultsWe modelled PVOD using mice with a homozygous deletion in gcn2. Gcn2-/- mice spontaneously develop increased right ventricular systolic pressure compared to wild-type controls (28.1[SD 3.4] vs. 24.7[SD 3.7] mmHg, p =0.04) mimicking human disease. Both left and right ventricles are hypertrophied in gcn2-/- mice, but left ventricular systolic pressures remain normal at baseline. We have previously observed inflammation in other forms of pulmonary hypertension and so measured serum cytokines levels and lung cytokine transcription in gcn2-/- mice. Inflammatory cytokines are raised in gcn2-/- mice at baseline in both serum and lung, and this is exaggerated after a pro-inflammatory stimulus (LPS). To gain mechanistic insight, we generated single cell suspensions from gcn2-/- mouse lungs and wild-type littermates and performed droplet-based single cell RNA sequencing. This revealed that GCN2 loss significantly perturbs the transcriptomes of neutrophils and B cells. These data are enabling us to identify disease-relevant signalling pathways and cell types.

BackgroundLymphangioleiomyomatosis (LAM) is a destructive monogenic disease in which clonal mTOR dysregulated mesenchymal ‘LAM cells’ recruit fibroblasts and immune cells forming discreet lung parenchymal nodules resulting in protease activation, lung cysts and respiratory failure. We hypothesised that mTOR driven senescence in LAM cells, induces senescence in adjacent LAM associated fibroblasts (LAF) in turn impairing ATII cell mediated repair of protease induced lung injury.MethodsWe examined LAM cell interactions using scRNAseq, laser microdissection, dual label immunohistochemistry, primary cell co-cultures and a TSC null murine homograft model.Resultsp21 and to a lesser extent, p16 proteins were increased in human LAM lung. Within LAM nodules p21 co-localised with both PNL2 (LAM cells) and PNL2 negative cells. Outside nodules, p21 co-localised with SPC (ATII cells). In immunocompetent LAM homograft models, senescence associated beta-galactosidase activity increased with time and was greater than control animals. scRNAseq of human LAM lungs showed alterations in ATII cell regulation of cell death, apoptotic pathways, senescence and Wnt signalling. A Stat 3/p53 dependent pathway governing apoptosis and alterations in lipolysis and ATI/II differentiation were present. In vitro LAM cell/LAF/epithelial co-cultures show that SA-beta-galactosidase activity and associated genes are upregulated in an mTOR dependent manner. Interrogation of scRNA seq data from nodules and epithelial areas validates these findings and is associated with lung function and disease duration in humans.ConclusionsmTOR dysregulated LAM cells induce fibroblast and epithelial senescence and reduce ATII cell viability to impair the repair response to lung injury.Please refer to page A210 for declarations of interest related to this abstract.