Prediction Of Treatment Effect Research Articles

#2801 Which patients may benefit most from hemodiafiltration compared with hemodialysis: prediction of the individualized treatment effect

Abstract Background and Aims Haemodiafiltration (HDF) with an appropriate dosing reduces death from any cause based on an individual participant data meta-analysis and aggregated data meta-analyses of available randomized trials. No differences in treatment effect are reported for specific patient populations when defined on a single characteristic in subgroup analyses. Yet, it is unclear to what extent individual patients, characterized by a combination of clinical characteristics, may benefit from HDF. This study aimed to develop and internally validate a treatment effect prediction model to determine which individual patients would benefit most from well dosed HDF, compared with haemodialysis (HD), in terms of survival. Method Individual participant data from five randomized controlled trials (CONTRAST, ESHOL, Turkish HDF study, French HDF study, CONVINCE) comparing HDF with HD on all-cause mortality were used to derive a Royston-Parmar model for exploring the prediction of absolute treatment effect of HDF based on pre-specified patient and disease characteristics, notably age, sex, body mass index, diabetes mellitus, history of cardiovascular disease, creatinine levels, and c-reactive protein levels. Internal validation of the model was performed using internal-external cross validation. Results Among 4153 participants, with a median follow-up of 30 months (Q1-Q3: 24-36), death from any cause occurred in 558 patients (27.2%). The median predicted survival benefit of HDF compared with HD was 6.9 (Q1–Q3: 5.6–9.0) months, with a range of 2 to 42 months. Patients who were predicted to benefit most from HDF were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine levels. Internal-external cross validation showed adequate discrimination and calibration. Conclusion All-cause mortality is reduced by HDF compared with HD in ESKD patients. Yet, the absolute survival benefit seems to considerably differ between individual patients. Our results suggest that the effects of HDF on absolute survival can be predicted accurately using a combination of readily available patient and disease characteristics. This prediction model approach exemplifies the potential of prediction algorithms in supporting clinical decision-making for the choice of treatment options. Validation and potential updates to the presented algorithm are necessary, along with a preferable randomized evaluation of its clinical impact before widespread implementation.

Prediction of Treatment Response and Effect on Clinical Manifestations for Normal Pressure Hydrocephalus of Sleep Related Breathing Disorders

Objectives: Sleep disorders and normal pressure hydrocephalus (NPH) are increasingly important issues observed in neurological disorders. However, the correlation between these diseases has not been sufficiently studied. Thus, we discuss the correlation between sleep disorders and the clinical features of NPH. Methods: Overall, 40 patients with idiopathic NPH were included in the study. To evaluate the sleep disorders and psychiatric problems, surveys were administered, including the 36-Item Short Form Survey, Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Berlin Questionnaire, STOP-Bang Questionnaire, Hospital Anxiety and Depression Scale (HADS), Morningness-Eveningness Questionnaire, and the REM Sleep Behavior Disorder Screening Questionnaire. To evaluate the severity of dementia and ataxia, all of the patients completed the Korean mini-mental state examination; Korea frontal assessment battery score, amd unified Parkinson’s disease rating scale-motor scores before and after cerebrospinal fluid (CSF) drainage. The Pearson’s chi-square test, independent Student’s t-test, Mann–Whitney U test, and linear regression analysis were used to analyze the relationship between sleep disorders and symptom improvement after CSF drainage. Results: Of the 40 patients, 21 patients had poor sleep quality (PSQI>5), 8 patients had insomnia (ISI≥8), 11 patients had daytime sleepiness (ESS≥11), 9 (STOP-Bang≥3) to 13 (Berlin≥2 categories) patients had sleep apnea, 13 patients were anxious (HAS≥8), and 27 patients were depressed (HDS≥8). A linear regression analysis showed that sleep apnea was significantly correlated with cognitive function, and insomnia was correlated with cognitive, motor, and frontal lobe functions. Additionally, patients with severe sleep apnea demonstrated a greater recovery of cognitive function after CSF drainage. Conclusions: Obstructive sleep apnea (OSA) is closely related to the clinical symptoms and treatment effectiveness of NPH. The diagnosis and proper treatment of OSA is expected to improve the prognosis of patients with NPH.

Association of clinical outcome and imaging endpoints in extensive ischemic stroke—comparing measures of cerebral edema

Abstract Objectives Ischemic edema is associated with worse clinical outcomes, especially in large infarcts. Computed tomography (CT)–based densitometry allows direct quantification of absolute edema volume (EV), which challenges indirect biomarkers like midline shift (MLS). We compared EV and MLS as imaging biomarkers of ischemic edema and predictors of malignant infarction (MI) and very poor clinical outcome (VPCO) in early follow-up CT of patients with large infarcts. Materials and methods Patients with anterior circulation stroke, large vessel occlusion, and Alberta Stroke Program Early CT Score (ASPECTS) ≤ 5 were included. VPCO was defined as modified Rankin scale (mRS) ≥ 5 at discharge. MLS and EV were quantified at admission and in follow-up CT 24 h after admission. Correlation was analyzed between MLS, EV, and total infarct volume (TIV). Multivariable logistic regression and receiver operating characteristics curve analyses were performed to compare MLS and EV as predictors of MI and VPCO. Results Seventy patients (median TIV 110 mL) were analyzed. EV showed strong correlation to TIV (r = 0.91, p < 0.001) and good diagnostic accuracy to classify MI (EV AUC 0.74 [95%CI 0.61–0.88] vs. MLS AUC 0.82 [95%CI 0.71–0.94]; p = 0.48) and VPCO (EV AUC 0.72 [95%CI 0.60–0.84] vs. MLS AUC 0.69 [95%CI 0.57–0.81]; p = 0.5) with no significant difference compared to MLS, which did not correlate with TIV < 110 mL (r = 0.17, p = 0.33). Conclusion EV might serve as an imaging biomarker of ischemic edema in future studies, as it is applicable to infarcts of all volumes and predicts MI and VPCO in patients with large infarcts with the same accuracy as MLS. Clinical relevance statement Utilization of edema volume instead of midline shift as an edema parameter would allow differentiation of patients with large and small infarcts based on the extent of edema, with possible advantages in the prediction of treatment effects, complications, and outcome. Key Points • CT densitometry–based absolute edema volume challenges midline shift as current gold standard measure of ischemic edema. • Edema volume predicts malignant infarction and poor clinical outcome in patients with large infarcts with similar accuracy compared to MLS irrespective of the lesion extent. • Edema volume might serve as a reliable quantitative imaging biomarker of ischemic edema in acute stroke triage independent of lesion size.

Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.

Left bundle branch block (LBBB) induced cardiomyopathy is an increasingly recognized disease entity. However, no clinical testing has been shown to be able to predict such an occurrence. A 70-year-old male with a prior history of LBBB with preserved ejection fraction (EF) and no other known cardiovascular conditions presented with presyncope, high-grade AV block, and heart failure with reduced EF (36%). His coronary angiogram was negative for any obstructive disease. No other known etiologies for cardiomyopathy were identified. Artificial intelligence-enabled ECGs performed 6 years prior to clinical presentation consistently predicted a high probability (up to 91%) of low EF. The patient successfully underwent left bundle branch area (LBBA) pacing with correction of the underlying LBBB. Subsequent AI ECGs showed a large drop in the probability of low EF immediately after LBBA pacing to 47% and then to 3% 2 months post procedure. His heart failure symptoms markedly improved and EF normalized to 54% at the same time. Artificial intelligence-enabled ECGS may help identify patients who are at risk of developing LBBB-induced cardiomyopathy and predict the response to LBBA pacing.

Individualized Treatment Effect Prediction with Machine Learning — Salient Considerations

BackgroundMachine learning–based approaches that seek to accomplish individualized treatment effect prediction have gained traction; however, some salient challenges lack wider recognition.MethodsWe describe key methodologic considerations for individualized treatment effect prediction models using data from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial for spironolactone in heart failure with preserved ejection fraction. The causal survival forest algorithm was used for model development. Calibration and discrimination were evaluated using a bootstrapping-based internal validation procedure. Observed benefits were described for predicted benefit quartiles and quartiles of a known effect modifier: ejection fraction. A negative control analysis with noncardiovascular death as the outcome was implemented to detect confounding.ResultsAmong 3445 participants, 671 events occurred over a median of 3.3 years of follow-up. In internal validation, a higher average observed benefit was noted among patients in the highest quartile of predicted benefit. The median (interquartile range) of the observed restricted mean survival time difference at 3.3 years at the highest quartile of model-predicted benefit was 62 days (32 to 83) and was 47 days (26 to 67) at the lowest quartile of ejection fraction. Body-mass index had higher contribution to prediction of benefit relative to other included measures (33.7% vs. glomerular filtration rate [27.3%], ejection fraction [15.1%], and younger age [12.8%]) No benefit was observed for noncardiovascular death at higher model-predicted benefit quartiles, although benefit for noncardiovascular death was observed at lower quartiles.ConclusionsCarefully applied and validated predictive models hold promise in identifying heterogeneous treatment effects and are useful for hypothesis generation regarding the role of phenotypic characteristics in modifying the benefit of experimental interventions in clinical trials. (Funded by the National Heart, Lung, and Blood Institute; ClinicalTrials.gov number, NCT00094302.)

Prediction of Treatment Effect of SLE-ITP Patients Based on Cost-Sensitive Neural Network and Variational Autoencoder.

The aim of the study was to examine the factors influencing the therapeutic effect of patients with systemic lupus erythematosus combined with immune thrombocytopenia (SLE-ITP) and develop a prediction model to predict the therapeutic effect of SLE-ITP. Three hundred twenty-four SLE-ITP patients were retrieved from the electronic health record database of SLE patients in Jiangsu Province according to the latest treatment response criteria for ITP. We adopted the Cox model based on the least absolute shrinkage and selection operator to explore the impact factors affecting patient therapeutic effect, and we developed neural network model to predict therapeutic effect, and in prediction model, cost-sensitivity was introduced to address data category imbalance, and variational autoencoder was used to achieve data augmentation. The performance of each model was evaluated by accuracy and the area under the receiver operator curve. The results showed that B-lymphocyte count, H-cholesterol level, complement-3 level, anticardiolipin antibody, and so on could be used as predictors of SLE-ITP curative effect, and abnormal levels of alanine transaminase, immunoglobulin A, and apolipoprotein B predicted adverse treatment response. The neural network treatment effect prediction model based on cost-sensitivity and variational autoencoder was better than the traditional classifiers, with an overall accuracy rate closed to 0.9 and a specificity of more than 0.9, which was useful for clinical practice to identify patients at risk of ineffective treatment response and to achieve better individualized management. By predicting the curative effect of SLE-ITP, the severity of patients can be determined, and then the best treatment strategy can be planned to avoid ineffective treatment.

Prediction of Response to Chemoradiotherapy by Dynamic Changes of Circulating Exosome Levels in Patients with Esophageal Squamous Cell Carcinoma.

The exosomes-based liquid biopsy represents a prospective biomarker for tumor screening, prognosis prediction, and tumor regression. This study aimed to isolate circulating exosomes (CEs) from plasma of the esophageal squamous cell carcinoma (ESCC) patients who received chemoradiotherapy through exosome detection method via the ultrafast-isolation system (EXODUS) and investigated the association between the dynamic changes of CE levels and therapeuticeffect. We isolated and quantitatively analyzed CEs from plasma of locally advanced ESCC patients received chemoradiotherapy at 2 time points: baseline (pre-chemoradiotherapy) and 2 months after the chemoradiotherapy (post-chemoradiotherapy). We isolated exosomes from plasma by EXODUS platform and confirmed them through nanoparticle tracking analysis (NTA), transmission electron microscope (TEM), and Western blot. The associations of CE level with clinicopathological characteristics, tumor regression, and progression-free survival (PFS) were analyzed. The average diameter of CEs was 107.4±14.3 nm at baseline and 101.7±17.1 nm at post-chemoradiotherapy. The mean exosome concentration significantly decreased after chemoradiotherapy (7.3×1011 particles/mL vs 5.4×1011 particles/mL, P < 0.001). The patients with stage III-IVA and tumor length ≥5cm had obviously higher baseline CE levels. Dynamic changes in CE levels were successfully applied for evaluation of chemoradiotherapy response and PFS. Furthermore, through multivariate Cox regression analysis, it was revealed that dynamic changes of CE levels were an independent predictor of PFS in locally advanced ESCC patients who received chemoradiotherapy. Here, we demonstrated EXODUS platform isolated and enriched CEs from plasma of ESCC patients with high-purity and high-yield. The EXODUS platform can facilitate liquid biopsy based on exosomes translation to the clinic. Baseline CE levels can reflect ESCC tumor burden. The dynamic changes of CE levels during chemoradiotherapy allow the prediction of treatment effect and PFS of ESCC patients, requiring further investigations in larger patient cohorts.

Machine learning models for early prediction of potassium lowering effectiveness and adverse events in patients with hyperkalemia

The aim of this study was to develop a model for early prediction of adverse events and treatment effectiveness in patients with hyperkalemia. We collected clinical data from patients with hyperkalemia in the First Hospital of Zhejiang University School of Medicine between 2015 and 2021. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to analyze the predictors on the full dataset. We randomly divided the data into a training group and a validation group, and used LASSO to filter variables in the training set. Six machine learning methods were used to develop the models. The best model was selected based on the area under the curve (AUC). Shapley additive exPlanations (SHAP) values were used to explain the best model. A total of 1074 patients with hyperkalemia were finally enrolled. Diastolic blood pressure (DBP), breathing, oxygen saturation (SPO2), Glasgow coma score (GCS), liver disease, oliguria, blood sodium, international standardized ratio (ISR), and initial blood potassium were the predictors of the occurrence of adverse events; peripheral edema, estimated glomerular filtration rate (eGFR), blood sodium, actual base residual, and initial blood potassium were the predictors of therapeutic effect. Extreme gradient boosting (XGBoost) model achieved the best performance (adverse events: AUC = 0.87; therapeutic effect: AUC = 0.75). A model based on clinical characteristics was developed and validated with good performance.

Systematic review and appraisal of quality, definitions and treatment recommendations of clinical guidelines for glaucoma suspects

AbstractBackgroundTo appraise the quality of clinical practice guidelines for glaucoma suspects, and to assess their consistency for how a ‘glaucoma suspect’ is defined and their recommendations for treatment initiation for such individuals.MethodsThis study included all documents that self‐identified as a ‘guideline’ and provided recommendation(s) for the clinical care of glaucoma suspects. The quality of eligible guidelines was assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument.ResultsFrom 1196 records retrieved from comprehensive searches and two records manually included, 20 clinical practice guidelines were deemed eligible. Based on an appraisal using the AGREE II instrument, 16 (80%) guidelines had ≤2 domains with scores &gt;66%. Overall, the lowest scoring domains were for applicability, editorial independence and stakeholder involvement. There was relatively poor agreement across the guidelines for what defines a ‘glaucoma suspect’ or ‘primary open angle glaucoma [POAG] suspect’, as well as the recommendations and criteria for treatment initiation in these populations. There was better agreement for the definition and recommendations for treatment initiation for ‘primary angle closure suspects’.ConclusionsThere is substantial room to improve the methodological quality of most current international clinical guidelines for glaucoma suspects. Clinicians should consider this finding when using such guidelines to inform their care of glaucoma suspects. Substantial variation in the definition of a POAG suspect and recommendations for treatment initiation underscores important gaps in the current evidence for the accurate prediction of glaucoma development and treatment effectiveness in these individuals.

Predictive markers for anti-inflammatory treatment response in thyroid eye disease.

Anti-inflammatory treatment is the primary and vital therapeutic approach for active, moderate-to-severe thyroid eye disease (TED). Accurate pretreatment prediction of treatment response is of paramount importance for the prognosis of patients. However, relying solely on the clinical activity score asa determinant of activity has led to unsatisfactory treatment outcomes. In recent years, significant advancements have been made in identifying predictive markers for anti-inflammatory treatment response in TED, clinical markers, body fluid biomarkers and imaging biomarkers. Several clinical studies have developed prediction models based on these markers. However, there is still a lack of comprehensive elucidation or comparison between the different markers. Therefore, this review aims to provide a detailed analysis of the definition, characteristics, and application of predictive markers for anti-inflammatory treatment response in TED. Through detailed literature search, 26 articles applying anti-inflammatory treatment effect prediction with a total of 1948 TED patients were used for analysis and discussion. By gaining a better understanding of the current research on predictive markers, we can accelerate and guide the exploration of treatment prediction strategies, leading us towards an era of precise therapy for TED.

High efficacy of particle beam therapies against tumors under hypoxia and prediction of the early stage treatment effect using 3'-deoxy-3'-[18F]fluorothymidine positron emission tomography

ObjectiveCompared with radiation therapy using photon beams, particle therapies, especially those using carbons, show a high relative biological effectiveness and low oxygen enhancement ratio. Using cells cultured under normoxic conditions, our group reported a greater suppressive effect on cell growth by carbon beams than X-rays, and the subsequent therapeutic effect can be predicted by the cell uptake amount of 3'-deoxy-3'-[18F]fluorothymidine (18F-FLT) the day after treatment. On the other hand, a hypoxic environment forms locally around solid tumors, influencing the therapeutic effect of radiotherapy. In this study, the influence of tumor hypoxia on particle therapies and the ability to predict the therapeutic effect using 18F-FLT were evaluated.MethodsUsing a murine colon carcinoma cell line (colon 26) cultured under hypoxic conditions (1.0% O2 and 5.0% CO2), the suppressive effect on cell growth by X-ray, proton, and carbon irradiation was evaluated. In addition, the correlation between decreased 18F-FLT uptake after irradiation and subsequent suppression of cell proliferation was investigated.ResultsTumor cell growth was suppressed most efficiently by carbon-beam irradiation. 18F-FLT uptake temporarily increased the day after irradiation, especially in the low-dose irradiation groups, but then decreased from 50 h after irradiation, which is well correlated with the subsequent suppression on tumor cell growth.ConclusionsCarbon beam treatment shows a strong therapeutic effect against cells under hypoxia. Unlike normoxic tumors, it is desirable to perform 18F-FLT positron emission tomography 2–3 days after irradiation for early prediction of the treatment effect.Graphical

Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity

BackgroundIn medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights.MethodsTo analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them.ResultsA numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model.ConclusionThe method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution.

An alternatively spliced PD-L1 isoform PD-L1∆3, and PD-L2 expression in breast cancers: implications for eligibility scoring and immunotherapy response.

Targeting PD-1/PD-L1 has shown substantial therapeutic response and unprecedented long-term durable responses in the clinic. However, several challenges persist, encompassing the prediction of treatment effectiveness and patient responses, the emergence of treatment resistance, and the necessity for additional biomarkers. Consequently, we comprehensively explored the often-overlooked isoforms of crucial immunotherapy players, leveraging transcriptomic analysis, structural modeling, and immunohistochemistry (IHC) data. Our investigation has led to the identification of an alternatively spliced isoform of PD-L1 that lacks exon 3 (PD-L1∆3) and the IgV domain required to interact with PD-1. PD-L1∆3 is expressed more than the canonical isoform in a subset of breast cancers and other TCGA tumors. Using the deep learning-based protein modeling tool AlphaFold2, we show the lack of a possible interaction between PD-L1∆3 and PD-1. In addition, we present data on the expression of an additional ligand for PD-1, PD-L2. PD-L2 expression is widespread and positively correlates with PD-L1 levels in breast and other tumors. We report enriched epithelial-mesenchymal transition (EMT) signature in high PD-L2 transcript expressing (PD-L2 > PD-L1) tumors in all breast cancer subtypes, highlighting potential crosstalk between EMT and immune evasion. Notably, the estrogen gene signature is downregulated in ER + breast tumors with high PD-L2. The data on PD-L2 IHC positivity but PD-L1 negativity in breast tumors, together with our results on PD-L1∆3, highlight the need to utilize PD-L2 and PD-L1 isoform-specific antibodies for staining patient tissue sections to offer a more precise prediction of the outcomes of PD-1/PD-L1 immunotherapy.

Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis

BackgroundA previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to identify patients that are most likely to benefit from antibiotics when applying conventional (i.e. univariable or one-variable-at-a-time) subgroup analysis. We updated the systematic review and investigated whether multivariable prediction of patient-level prognosis and antibiotic treatment effect may lead to more tailored treatment assignment in adults presenting to primary care with ARS.MethodsAn IPD-MA of nine double-blind placebo-controlled trials of antibiotic treatment (n=2539) was conducted, with the probability of being cured at 8–15 days as the primary outcome. A logistic mixed effects model was developed to predict the probability of being cured based on demographic characteristics, signs and symptoms, and antibiotic treatment assignment. Predictive performance was quantified based on internal-external cross-validation in terms of calibration and discrimination performance, overall model fit, and the accuracy of individual predictions.ResultsResults indicate that the prognosis with respect to risk of cure could not be reliably predicted (c-statistic 0.58 and Brier score 0.24). Similarly, patient-level treatment effect predictions did not reliably distinguish between those that did and did not benefit from antibiotics (c-for-benefit 0.50).ConclusionsIn conclusion, multivariable prediction based on patient demographics and common signs and symptoms did not reliably predict the patient-level probability of cure and antibiotic effect in this IPD-MA. Therefore, these characteristics cannot be expected to reliably distinguish those that do and do not benefit from antibiotics in adults presenting to primary care with ARS.

Using an enhanced ant lion optimizer to improve artificial neural networks for the prediction of food-grade sodium alginate treatment effects for ready-mixed concrete plant wastewater

Using an enhanced ant lion optimizer to improve artificial neural networks for the prediction of food-grade sodium alginate treatment effects for ready-mixed concrete plant wastewater

Prediction of Cancer Treatment Effectiveness and Patient Outcomes using Machine Learning Classification Approaches - A Review

This study systematically reviews the Machine Learning methods developed to help predict the patient outcome and treatment effectiveness in cancer treatment. This research paper has been drafted from several other similar papers and with the help of a few topics related websites providing information regarding the radiation toxicity, survival rate and tumor response. Which are the main classification criteria for the patients. The use of ANN, DT, SVM and BNs has proved to be very beneficial in the classification of any given dataset, the accuracy of the model will be high with the use of all these ML methods.

Inflammatory risk stratification individualizes anti-inflammatory pharmacotherapy for acute type A aortic dissection

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016-2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%-41.6%; RR 1.196 [0.698-2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816-1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

Personalized medicine: Somatosensory phenotyping in musculoskeletal pain conditions.

Current clinical phenotyping of musculoskeletal pain provides very limited evidence- based support to personalized medicine. This paper discusses the potential of somatosensory phenotyping to contribute to personalized medicine for prognosis and prediction of treatment effects. Highlight of definitions and regulatory requirements for phenotypes and biomarkers. Appraisal of the literature on somatosensory phenotyping in musculoskeletal pain. Somatosensory phenotyping can identify clinical conditions and manifestations that may affect treatment decisions. However, studies have shown inconsistent associations of phenotyping measures with clinical outcomes, and the strength of association is mostly weak. Most somatosensory measures have been developed for research, are too demanding to find large acceptance in clinical settings, and have uncertain clinical usefulness. Current somatosensory measures will unlikely be validated as strong prognostic or predictive biomarkers. However, they still have the potential to support personalized medicine. Including somatosensory measures in biomarker signatures, that is, a set of measures that are collectively associated with outcomes, is potentially more useful than aiming to the identification of single biomarkers. Furthermore, somatosensory phenotyping may be introduced as part of patient's evaluation to contribute to better-informed and personalized treatment decisions. To this purpose, a change in the way research currently approaches somatosensory phenotyping is warranted. A pathway is proposed that involves: (1) the identification of clinically applicable measures that are specific to clinical conditions; (2) the association of somatosensory phenotypes with outcomes; (3) multi-site replication; and (4) the determination of clinical benefits in randomized controlled trials. Somatosensory phenotyping has the potential to support personalized medicine. However, current measures do not seem to meet the criteria for being strong prognostic or predictive biomarkers, most of them are too demanding to find large acceptance in clinical settings, and their clinical usefulness has not been proven. The value of somatosensory phenotyping can be more realistically determined by re-orienting research to the development of simplified testing protocols, applicable to large-scale clinical practice, and tested for clinical usefulness in randomized controlled trials.

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis.

Machine learning is now well-developed in non-small cell lung cancer (NSCLC) radiotherapy. But the research trend and hotspots are still unclear. To investigate the progress in machine learning in radiotherapy NSCLC, we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future. The involved researches were obtained from the Web of Science Core Collection database (WoSCC). We used R-studio software, the Bibliometrix package and VOSviewer (Version 1.6.18) software to perform bibliometric analysis. We found 197 publications about machine learning in radiotherapy for NSCLC in the WoSCC, and the journal Medical Physics contributed the most articles. The University of Texas MD Anderson Cancer Center was the most frequent publishing institution, and the United States contributed most of the publications. In our bibliometric analysis, "radiomics" was the most frequent keyword, and we found that machine learning is mainly applied to analyze medical images in the radiotherapy of NSCLC. The research we identified about machine learning in NSCLC radiotherapy was mainly related to the radiotherapy planning of NSCLC and the prediction of treatment effects and adverse events in NSCLC patients who were under radiotherapy. Our research has added new insights into machine learning in NSCLC radiotherapy and could help researchers better identify hot research areas in the future.

Right-side frontal-central cortical hyperactivation before the treatment predicts outcomes of antidepressant and electroconvulsive therapy responsivity in major depressive disorder

Major depressive disorder places a great burden on healthcare resources worldwide. Antidepressants are the first-line treatment for major depressive disorder, but if patients don't respond adequately, brain stimulation therapy may be needed as second-line treatment. Digital phenotyping in patients with major depressive disorder will aid in the timely prediction of treatment effectiveness. This study explored electroencephalographic (EEG) signatures that diversify depression treatment responsivity, including antidepressant administration or brain stimulation therapy. Resting-state, pre-treatment EEG sequences from depressive patients who received fluoxetine treatment (n = 55; 26 remitters and 29 poor responders) or electroconvulsive therapy (ECT, n = 58; 36 remitters and 22 nonremitters) were recorded on 19 channels. Twenty-nine EEG segments were obtained from each patient per recording electrode. Power spectral analysis was conducted for feature extraction and showed the highest predictive accuracy for fluoxetine or ECT outcomes. Both occurred with beta-band oscillations within right-side frontal-central (F1-score = 0.9437) or prefrontal areas of the brain (F1-score = 0.9416), respectively. Significantly higher beta-band power was observed among patients who lacked adequate treatment response than the remitters, specifically at 19.2 Hz or 24.5 Hz for fluoxetine administration or ECT outcome, respectively. Our findings indicated that pre-treatment, right-side cortical hyperactivation is associated with poor outcomes of antidepressant-based or ECT-based treatment in major depression. Whether depression treatment response rates can be improved by reducing the high-frequency EEG power in corresponding areas of the brain to provide a protective effect against depression recurrence warrants further study.