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TIDE analysis | From the TIDE website, we obtained the UCEC patient's TIDE scoring file. Upon analysis, we observed variations in TIDE scores between the high- and low-risk groups (Fig. | PMC10721879 | ||
Drug susceptibility analysis | UCEC | A total of 19 drugs with unique IC50 values were identified by comparing the IC50 values of various medications used to treat UCEC in high- and low-risk patients. Cisplatin, Foretinib, NG-25, TG101348, and WH-4-023 exhibited lower IC50 values and displayed a negative correlation with risk scores when administered to patients in the high-risk category (Fig. Drug susceptibility analysis. ( | PMC10721879 | |
Discussion | endometrial cancer, tumor, cancer, UCEC, cholangiocarcinoma | CANCER, ENDOMETRIAL CANCER, CHOLANGIOCARCINOMA, TUMOR | The prevalence of endometrial cancer is on the rise, and although surgery usually leads to positive outcomes for early-stage casesUsing 14 DALPMs, we constructed a prognostic model to forecast the patients' prognosis in this research. By conducting co-expression analysis, a set of 1,136 lncRNAs linked to 10 disulfide death genes were extracted to acquire DALPM suitable for prognostic modeling. We obtained the 14 DALPMs for building our prognostic risk model through LASSO, univariate Cox, and multivariate Cox analyses. After examining these 14 DALPMs, we identified U91328.1, AC244517.7, AC009779.2, AC090617.5, AC093382.1, and BOLA3-AS1 as potential prognostic indicators for specific types of cancerTo assess the necessity and accuracy of our model, we categorized patients into high and low risk groups by calculating the risk score using DALPMs. The KM curves for OS and PFS demonstrated a clear distinction in survival rates between patients, which implies that it is important to consider the treatment and prognosis of patients classified as high risk. We also conducted univariate and multivariate Cox analyses, revealing that the risk score independently predicts the prognosis of UCEC and generated ROC curves to compare the risk scores with other clinical factors currently in use. According to the ROC curves, the risk score exhibited the highest AUC value, indicating that utilizing the risk score as a predictor may provide a relatively more precise assessment of the prognosis for patients with UCEC. By combining risk scores with other clinical factors, the nomogram and calibration curve were created to estimate the 1-, 3-, and 5-year survival rates for patients. Furthermore, the risk scores can be used to differentiate the survival outcomes of patients with varying clinical stages. The precision examinations for our model produced extremely pleasing outcomes, suggesting its capability to precisely forecast the prognosis of patients diagnosed with UCEC.The concept of TME, the existence of benign cells and constituents within tumor cellsDespite the absence of a general disparity in immune cell scores, a closer examination revealed discrepancies in particular immune cell populations, including memory B cells and activated CD4 memory T cells, among the high and low risk groups. The immune cells that are expressed differently in the TME could serve as potential targets for therapeutic interventions in UCEC. Immune cells with the ability to adapt, like B cells and T cells, have a significant impact on the TMEWe examined the immune pathways that exhibited variations among patients in the two groups and observed variations in aDCs, APC-co-inhibition, macrophages, parainflammation, and Type-I-IFN-Reponse. Type-I-IFN encompasses a broad class of inflammatory cytokines released when the immune system is weakenedTumor mutational burden reflects the genetic mutation in patients. The examination of tumor mutation adherence indicated that the low-risk group exhibited a greater TMB compared with the high risk group, signifying a disparity in TMB between the two groups. PTEN, which plays a significant role in the advancement and therapy of cancer, exhibited the highest mutation rate among both groups. The inhibition of PTEN can enhance the release of exosomes and the spread of cholangiocarcinoma by impeding TFEB-mediated formation of lysosomesThe analysis of TIDE, which investigates the possibility of tumor evasion during immunotherapy, uncovered variations in TIDE scores among the groups at high and low risk. The group at increased risk demonstrated elevated TIDE scores, suggesting a higher probability of immune evasion during immunotherapy. This aligns with our earlier TME analysis. In addition, we performed drug susceptibility analysis to compare the effectiveness of medications among patients categorized as high and low risk groups. The findings from the drug sensitivity analysis revealed variances in the IC50 values of a total of 19 drugs among patients in both groups. Better treatment effects were observed in patients in the high-risk group when using drugs with lower IC50 values, such as Cisplatin, Foretinib, NG-25, TG101348, and WH-4-023, among the screened medications; therefore, these drugs deserve further attention. Our results indicate that our model can predict the prognosis and response to immunotherapy in patients with UCEC, potentially assisting in clinical decision-making.In conclusion, disulfidptosis is a recently discovered form of cellular demise that is linked to cancer. lncRNAs affect cellular biological processes, consequently influencing the treatment of cancer. At present, there is still a lack of research on the disulfidptosis related lncRNAs in UCEC. By utilizing experimental and validation cohorts, our bioinformatics research allows for the investigation of prognostic biomarkers associated with disulfidptosis in UCEC while ensuring the model's reliability. We believe that our research provides a new perspective for the study of UCEC, and we believe that as the research continues to deepen, the prognostic markers we have identified will provide some guidance for clinical practice. We also believe that with the improvement of relevant experiments, this model has sufficient potential to be transformed into clinical tools to serve more patients. For example, we can quantify the expression of LncRNA within the model in the patient's body to comprehensively assess the patient's risk, estimate the patient's prognosis, and provide targeted guidance for treatment. However, our research may have the following limitations to some extent. We searched for UCEC-related datasets in the GEO database and found no dataset that met our analysis criteria. Therefore, we randomly organized the datasets from the TCGA repository to validate the precision of our findings. Furthermore, additional preclinical investigations are required to enhance the model's reliability prior to its implementation in clinical settings. | PMC10721879 |
Conclusion | UCEC, tumor | TUMOR | In this study, we discovered 14 different DALPMs and utilized them to create a predictive model. The prognostic predictions for patients with UCEC can be accurate using this model, which has significant connections with tumor immunity and can partially guide UCEC treatment decisions. Our results provide a novel approach for future related studies. | PMC10721879 |
Supplementary Information |
Supplementary Information. | PMC10721879 | ||
Supplementary Information | The online version contains supplementary material available at 10.1038/s41598-023-49750-6. | PMC10721879 | ||
Acknowledgements | TCGA | The authors express their appreciation to the TCGA and UCSC Xena databases for providing access to the data. | PMC10721879 | |
Author contributions | J.S. conceived, designed, and supervised the study. B.L. and X.L. performed data analysis and drafted the manuscript. M.M. and Q.W. collected the data, B.L. arranged the figures. C.W. revised the manuscript. All authors reviewed and approved the final manuscript. B.L., X.L., M.M. and Q.W. contributed equally. | PMC10721879 | ||
Funding | This study was funded by the China Postdoctoral Science Foundation (2022M712388), Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A) and Nature Science Foundation of Inner Mongolia (2023LHMS08016). | PMC10721879 | ||
Data availability | The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. | PMC10721879 | ||
Competing interests | The authors declare no competing interests. | PMC10721879 | ||
References | PMC10721879 | |||
Background | Acute pancreatitis, sepsis | ACUTE PANCREATITIS, REGRESSION, SEPSIS | This study aimed to construct predictive models for the risk of sepsis in patients with Acute pancreatitis (AP) using machine learning methods and compared optimal one with the logistic regression (LR) model and scoring systems. | PMC10474758 |
Methods | REGRESSION | In this retrospective cohort study, data were collected from the Medical Information Mart for Intensive Care III (MIMIC III) database between 2001 and 2012 and the MIMIC IV database between 2008 and 2019. Patients were randomly divided into training and test sets (8:2). The least absolute shrinkage and selection operator (LASSO) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors. Based on the selected predictive factors, 6 machine learning models were constructed, including support vector machine (SVM), K-nearest neighbour (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT) and adaptive enhancement algorithm (AdaBoost). The models and scoring systems were evaluated and compared using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the area under the curve (AUC). | PMC10474758 | |
Results | sepsis, SIRS, Coma, organ failure | ACUTE PANCREATITIS, SEPSIS, COMA, SYSTEMIC INFLAMMATORY RESPONSE SYNDROME | A total of 1, 672 patients were eligible for participation. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The predictive factors for the risk of sepsis in AP patients included age, insurance, vasopressors, mechanical ventilation, Glasgow Coma Scale (GCS), heart rate, respiratory rate, temperature, SpO2, platelet, red blood cell distribution width (RDW), International Normalized Ratio (INR), and blood urea nitrogen (BUN). The AUC of the GBDT model for sepsis prediction in the AP patients in the testing set was 0.985. The GBDT model showed better performance in sepsis prediction than the LR, systemic inflammatory response syndrome (SIRS) score, bedside index for severity in acute pancreatitis (BISAP) score, sequential organ failure assessment (SOFA) score, quick-SOFA (qSOFA), and simplified acute physiology score II (SAPS II). | PMC10474758 |
Conclusion | sepsis, SIRS | SEPSIS | The present findings suggest that compared to the classical LR model and SOFA, qSOFA, SAPS II, SIRS, and BISAP scores, the machine learning model-GBDT model had a better performance in predicting sepsis in the AP patients, which is a useful tool for early identification of high-risk patients and timely clinical interventions. | PMC10474758 |
Supplementary Information | The online version contains supplementary material available at 10.1186/s12893-023-02151-y. | PMC10474758 | ||
Keywords | PMC10474758 | |||
Background | sepsis, gastrointestinal diseases, SIRS, organ failure | ACUTE PANCREATITIS, GASTROINTESTINAL DISEASES, DISEASE OF THE PANCREAS, ACUTE PANCREATITIS, SEPSIS | Acute pancreatitis (AP), an inflammatory disease of the pancreas, is the leading cause of hospital admissions for gastrointestinal diseases worldwide [Several scoring systems have been identified to predict the severity and prognosis of AP and sepsis, including the SIRS score, bedside index for severity in acute pancreatitis (BISAP) score, sequential organ failure assessment (SOFA) score, quick-SOFA (qSOFA), simplified acute physiology score II (SAPS II) [Herein, this study aimed to (1) construct predictive models for the risk of sepsis in patients with AP using machine learning methods and validate the predictive performances; (2) select the optimal machine learning model and compare it with the LR model and scoring systems. This study may help to identify the risk of sepsis in patients with AP at an early stage and assist in the clinical treatment of AP and the prevention of sepsis. | PMC10474758 |
Methods | PMC10474758 | |||
Data design and study population | This study was a retrospective cohort study. Data were collected from Medical Information Mart for Intensive Care III (MIMIC III) database ( | PMC10474758 | ||
Data extraction | effusion | EFFUSION | Data collected from the database including (1) baseline characteristics: age (years), gender (male), Race (Black, White, and other), insurance (government, private, and unknown), marital status (divorced, married, separated, single, widowed, and unknown), interventions (vasopressors, mechanical ventilation), and effusion; (2) vital signs: heart rate (bpm), respiratory rate (breaths/minute), temperature (°C), SpO | PMC10474758 |
Variable definition | Sepsis | DISEASES, SEPSIS, DYSFUNCTION | Patients diagnosed with AP were determined by using the International Classification of Diseases (ICD) (ninth edition, code 577.0 or 10th version, code K 85.0) codes. Sepsis was diagnosed according to the sepsis-3 criteria [SOFA score calculated the dysfunction of six organ systems and the severity of the dysfunction, including the respiratory, coagulation, liver, cardiovascular, kidney, and nervous systems with a score of 0–4 for each item and a total score of 0–24 [ | PMC10474758 |
Outcome and follow-up | sepsis | SEPSIS | The outcome of the study was the risk of sepsis. Follow-up was conducted during hospitalization in the ICU and the end point of follow-up was sepsis or discharge from the ICU. The mean follow-up time was 3.64 (1.93–9.70) days. | PMC10474758 |
Construction and performances assessment of the machine learning models | The patients were randomly divided into two groups, of which 80% were used as the training set and the remaining 20% as the testing set. Based on the predictive factors selected, 6 machine learning models were constructed including support vector machine (SVM), K-nearest neighbor (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT), and adaptive enhancement algorithm (AdaBoost). The models were evaluated and compared by sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), accuracy, and the AUC of the ROC. | PMC10474758 | ||
Sample size calculation for predictive models | Our sample size calculation aimed to ensure a precise estimation of model parameters while minimizing the potential of overfitting. In order to achieve the goal of an average absolute prediction error (MAPE) of 0.05, as suggested by Riley et al. [ | PMC10474758 | ||
Statistical analyses | SIRS | REGRESSION | Variables with more than 20% missing values were excluded from further analysis. Random forest imputation was used to deal with missing data below 20%. Random forest imputation is a nonparametric algorithm that accommodates nonlinearities and interactions and does not require the specification of a specific parametric model [The least absolute shrinkage and selection operator (LASSO) (“LassoCV” method in Sklearn) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors and selected the best alpha = 0.0075 when one standard error of the minimum mean squared error (MSE) was used as a screening criterion. In order to select the optimal model from the 6 machine learning models, Delong’s test was used. Comparing the performance of the optimal machine learning model with LR, scoring systems (SOFA, qSOFA, SIRS, SAPS II, and BIASP). Clinical benefit was assessed using Decision Curve Analysis (DCA). A | PMC10474758 |
Results | PMC10474758 | |||
Basic characteristics of the study population | comorbidity, Sequential organ failure, coma, SIRS | BLOOD, SYSTEMIC INFLAMMATORY RESPONSE SYNDROME, COMA, ACUTE PANCREATITIS | A total of 1,930 participants diagnosed with AP were screened from MIMIC III and MIMIC IV databases; of these 1,930 patients, 256 were excluded due to the length of ICU stay less than 24 h, and 2 were excluded due to the age < 18 years. Finally, 1, 672 patients were eligible for participation, with 1,338 patients in the training set and 334 patients in the testing set. The flow chart of the participants’ selection is depicted in Fig.
The flow chart of the participants selection
Basic characteristics of study populationNotes: GCS: Glasgow coma scale; CCI: Charlson comorbidity index; WBC: White blood cell count; Platelet: Platelet count; RDW: Red blood cell distribution width; INR: International normalized ratio; PT: Prothrombin time; PTT: Partial thromboplastin time; BUN: Blood urea nitrogen; SOFA: Sequential organ failure assessment score; qSOFA: Quick SOFA; SAPSII: Simplified acute physiology score II; SIRS: Systemic inflammatory response syndrome; BISAP: Bedside index of severity in acute pancreatitis; M: Median; SD: Standard deviations; Q1: 25% Quantile; Q3: 75% Quantile | PMC10474758 |
Predictive factors selection for the risk of sepsis in AP patients | sepsis | REGRESSION, SEPSIS | After LASSO regression selection with 5-fold cross-validation via minimum criteria, 13 variables remain as the predictive factors for the risk of sepsis in AP patients: age, insurance, vasopressors, mechanical ventilation, GCS, heart rate, respiratory rate, temperature, SpO
The loss curves for the MSE loss with different Lambda
The SHAP plot of the relationship between the value of features and their impact on the model prediction | PMC10474758 |
Construction and performance validations of machine learning models | REGRESSION, POSITIVE | Based on the predictive factors, 6 machine learning models were constructed. The AUC value in the training set of the GBDT model was 0.994 [95% confidence interval (CI): 0.988 to 1.000], higher than the AUC value of the LR model (0.890, 95% CI: 0.860 to 0.920), AdaBoost model (0.918, 95% CI: 0.894 to 0.941), SVM model (0.912, 95% CI: 0.888 to 0.936), KNN model (0.908, 95% CI: 0.883 to 0.933), and MLP model (0.948, 95% CI: 0.929 to 0.967). In the testing set, GBDT had the highest AUC value (0.985, 95% CI: 0.966 to 1.000), thereby, GBDT was selected as the final predictive model. The ACU of the GBDT model (0.985, 95% CI: 0.966 to 1.000) was higher than the LR model (0.896, 95% CI: 0.841 to 0.951), achieving statistical significance (
Construction and performance validations of machine learning modelsNotes: SVM: Support vector machine; KNN: K-nearest neighbor; MLP: multi-layer perceptron; LR: logistic regression; GBDT: gradient boosting decision tree; AdaBoost: adaptive enhancement algorithm; PPV: Positive predictive values; NPV: Negative predictive values; AUC: Area under curve; CI: confidence interval; Ref: Reference | PMC10474758 | |
Comparisons of the predictive performances of the GBDT model with LR model, SOFA, qSOFA, SAPS II, SIRS, and BISAP scores | sepsis, Sequential organ failure, SIRS | SEPSIS, SYSTEMIC INFLAMMATORY RESPONSE SYNDROME, ACUTE PANCREATITIS, POSITIVE | In the testing set, the GBDT model achieved the best performance with an AUC of (0.985, 95% CI: 0.966 to 1.000) compared with qSOFA score (AUC: 0.780, 95% CI: 0.709 to 0.852,
Comparisons of the predictive performances of the GBDT model with LR model, SOFA, qSOFA, SAPS II, SIRS, and BISAP scoresNotes: GBDT: gradient boosting decision tree; SOFA: Sequential organ failure assessment score; qSOFA: Quick SOFA; SAPSII: Simplified acute physiology score II; SIRS: Systemic inflammatory response syndrome; BISAP: Bedside index of severity in acute pancreatitis; PPV: Positive predictive values; NPV: Negative predictive values; AUC: Area under curve; CI: confidence interval; Ref: Reference
The ROC curve comparison between GBDT and LR models and scoring systems
The net benefit of GBDT model, LR model, and scoring systems at different threshold probabilities for predicting sepsis in AP patients | PMC10474758 |
Discussion | sepsis, organ failure, MIMIC-III, intracranial aneurysms | RECURRENCE, REGRESSION, SEPSIS, DISEASES, INTRACRANIAL ANEURYSMS | In this retrospective study, we developed and validated machine learning-based models for predicting sepsis in AP patients. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The results of this study showed that the GBDT model had an excellent performance in the prediction of sepsis in patients with AP, with the AUC in the testing set at 0.985. Furthermore, the GBDT model achieved better predictive performance for sepsis prediction in AP patients compared with the LR model, and scoring systems.Advanced machine learning methods are good at dealing with high-order interactions and fitting complex nonlinear relationships, and can be used to integrate large amounts of data from electronic health records (EHRs). The application of machine learning to data-driven analysis shows promise for improving predictive performance in healthcare [The results of this study showed that the GBDT model had an excellent performance in predicting sepsis in AP patients. The GBDT model has been applied to diagnose and predict the outcomes of several diseases. A study that developed and assessed machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms found that the GBDT model showed an optimal prediction performance for predicting recurrence risk in patients with intracranial aneurysms after endovascular treatment in 6 months [GBDT is an ensemble algorithm widely used for regression and classification tasks. The GBDT algorithm creates multiple weak learners or individual trees by bootstrapping training samples and integrates their outputs to make predictions. The GBDT algorithm is less sensitive to hyperparameters, less prone to overfitting, and easy to implement. For the practical applicability of the GBDT model in a clinical setting, an example of how SHAP can be used locally to explain individual prediction was provided (Supplementary Fig. This study suggested that the basic characteristics of patients (age, temperature, and insurance) and vital signs (heart rate, respiratory rate, and SpO2 were associated with the risk of sepsis in AP. A study by Hong et al. indicated that age may be useful for predicting the development of persistent organ failure in patients with AP [Our study has several strengths. To the best of our knowledge, we first report the application of machine learning models to predict the risk of sepsis in AP patients using the MIMIC database. The optimal model was screened using a variety of machine learning methods and showed significantly better predictive value than LR and scoring systems, providing a basis for the accurate prediction of sepsis risk in AP patients. The sample size in this study is very sufficient for the construction and validation of prediction models. A larger sample size is valuable for developing a more robust prediction model, which has good generalization ability and good statistical efficacy for a wider population. However, the study was still subject to some limitations. First, the retrospective nature of the study may have introduced unavoidable selection bias, which limits the interpretation of the results. Second, the MIMIC data were obtained from a single center in the United States, which may affect the generalizability of the prediction model to other populations. The results may not be representative of the entire population of AP patients, although we attempted to provide detailed information in our study. Third, the study included AP patients in MIMIC-III and IV, which included hospitalized patients from 2001 to 2019. The population studied here is not consecutive and therefore different biases may have been introduced. As treatment regimens are developed and optimized, consistency of treatment regimens cannot be guaranteed, which may introduce some bias into the results. Fourth, radiological results in AP, specific chemoradiotherapy information, and medication dosage in vasopressors and mechanical ventilation may have an impact on our results, but the lack of radiological data in the database prevented us from performing further analyses. Fifth, the study lacked external validation. External validation is crucial to assess the generalizability and reliability of the model, especially when using data from a single center. Therefore, it would be important to perform further validation on an independent dataset in future studies to examine the robustness and generalization ability of the proposed model, which might greatly increase the impact of the current finding. Future research will need to explore other machine learning algorithms for predicting sepsis in AP patients. | PMC10474758 |
Conclusions | sepsis | SEPSIS | This study constructed and validated machine learning models to predict sepsis in patients with AP. The GBDT model, based on 13 predictive factors, showed promising performance in predicting sepsis in AP patients. A prediction model is a useful tool for the early identification of high-risk patients and timely clinical intervention. | PMC10474758 |
Acknowledgements | Not applicable. | PMC10474758 | ||
Authors’ contributions | FL and SS designed the study. FL wrote the manuscript. JY and CL collected, analyzed, and interpreted the data. SS critically reviewed, edited, and approved the manuscript. All authors read and approved the final manuscript. | PMC10474758 | ||
Funding | Not applicable. | PMC10474758 | ||
Data Availability | The datasets generated and/or analyzed during the current study are available in the MIMIC III database ( | PMC10474758 | ||
Declarations | PMC10474758 | |||
Ethics approval and consent to participate | The requirement of ethical approval for this was waived by the Institutional Review Board of Tianjin Medical University General Hospital, because the data was accessed from MIMIC III database and MIMIC IV database (publicly available database). The need for written informed consent was waived by the Institutional Review Board of Tianjin Medical University General Hospital due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations. | PMC10474758 | ||
Consent for publication | Not applicable. | PMC10474758 | ||
Competing interests | The authors declare no competing interests. | PMC10474758 | ||
Abbreviations | COMA, INFLAMMATORY RESPONSE | Acute pancreatitissystemic inflammatory response syndromesequential organ failure assessmentquick-SOFAsimplified acute physiology score IIlogistic regressionarea under the receiverAUC of the operating characteristic curveacute kidney injuryMedical Information Mart for Intensive Care IIIsystolic blood pressurediastolic blood pressureGlasgow Coma Scalecharlson comorbidity indexInternational Normalized Ratiowhite blood cellprothrombin timepartial thromboplastin timeblood urea nitrogenInternational Classification of Diseasessupport vector machineK-nearest neighbormulti-layer perceptrongradient boosting decision treeadaptive enhancement algorithmpositive prediction valuenegative prediction valuestandard deviations | PMC10474758 | |
References | PMC10474758 | |||
1. Introduction | HT | HEAT, SECONDARY | Background: Athletes training in heat experience physiological and perceptual symptoms that risk their safety and performance without adaptation. Purpose: We examined the changes in environmental symptoms, assessed with the Environmental Symptoms Questionnaire (ESQ), during heat acclimatization (HAz), heat acclimation (HA), and intermittent heat training (HT). Methods: Twenty-seven participants (mean ± standard deviation [M ± SD], age of 35 ± 12 y, VOEnvironmental conditions, such as high heat and humidity, place a considerable amount of strain on athletes, requiring them to adapt to various circumstances to maintain their safety and to achieve optimal performance [Researchers have found that the symptoms of heat strain, measured using the ESQ and TS, are exacerbated significantly following exercise in the heat [Athletes can best prepare their bodies and enhance their ability to adapt to extreme environmental conditions, such as high ambient temperature, through processes such as heat acclimatization (HAz) and heat acclimation (HA) [Through previous research, it has been found that ESQ symptoms are exacerbated during the initial days of an HA protocol and are improved during the following days [No previous research has investigated the effects of intermittent heat training (HT) or of combined acclimatization and acclimation on ESQ symptoms. Therefore, the primary purpose of this study was to investigate the changes in ESQ symptoms over the course of Haz, HA, and HT. We hypothesized that, if an individual’s perceptions, as measured by the ESQ, accurately reflected the progression of their physiological adaptations independent of breaks in training and of the nature of environmental exposure (i.e., laboratory vs. natural), we would observe a higher ESQ score post-trial versus pre-trial regardless of the intervention and that post-trial symptoms would incrementally decrease over the course of HAz, HA, and HT, respectively. A secondary aim of this study was to assess the relationships between ESQ symptoms and TS, HR, and T | PMC9962616 |
2. Materials and Methods | PMC9962616 | |||
2.1. Ethical Approval | HT | HEAT | Procedures in this study were approved by the <<removed for review>> Institutional Review Board. Participants provided both written and informed consent and were medically cleared prior to participation. This study took place in <<removed for review>>. Data presented within this manuscript are part of a larger study that focused on physiological and performance measures relative to this heat training protocol; however, the current study investigated different hypotheses and data focusing on environmental stress symptoms reported in the ESQ before and after heat stress trials during HAz, HA, and HT [ | PMC9962616 |
2.2. Participants | HT | HEAT | Twenty-seven aerobically fit males (age of 35 ± 12 y, body mass of 72.6 ± 8.8 kg, VOParticipants completed a baseline trial at the start of the study and then trained autonomously during the summer with no specific instruction regarding duration, intensity, or modality (Haz of 109 ± 9 days). During HAz, participants recorded each training session using their preferred wearable technology. Several variables, including total distance covered, training time, and average HR, and environmental conditions, including ambient temperature, relative humidity, and WBGT, were recorded. After HAz, participants performed a heat stress test to examine adaptations that resulted from HAz (post-HAz). Then, participants completed 5-day HA induction following HAz. The HA sessions involved participants exercising at a hyperthermic internal body temperature (between 38.50 °C and 39.75 °C) for 60 min. To do this, participants began exercising at a higher intensity (~ 70% vVOPost-HA, participants performed 8 weeks of HT. Participants were randomly assigned to 3 groups during HT, completing HT 2x per week (HTThis figure indicates the timeline and description of different phases of this study along with measurements that were collected during each of the heat stress trials. All measurements listed for heat stress trial 1 were collected during every heat stress trial. | PMC9962616 |
2.3. Statistical Analyses | HT | All data were assessed for normality and sphericity prior to analyses using SPSS (IBM version 26.0). Repeated measures ANOVAs assessed changes in pre-trial, post-trial, and post-pre-trial ESQ scores over the course of HAz, HA, and HT. Data from ANOVAs were reported as | PMC9962616 | |
3. Results | PMC9962616 | |||
3.1. HAz and HA | As previously mentioned, these data were part of a larger study that specifically investigated the physiological effects occurring in HR, Trec, and sweat rate (SR) as well as the performance measures, and more detailed analyses can be found in [Decreases in HR and T | PMC9962616 | ||
3.2. ESQ Symptoms | HT | HEAT | No significant differences in pre-ESQ symptoms occurred during HAz, HA, or HT (The symptoms that were rated with the highest scores consistently among all three trials following each stage of heat exposure (HAz, HA, and HT) were “I feel thirsty”, “I feel tired”, and “I feel hot”. No statistically significant differences were found between individual symptoms (Changes in ESQ symptoms were calculated by subtracting the pre-trial values from the post-trial values. Changes in ESQ symptoms improved significantly throughout induction (HAz, HA) ( | PMC9962616 |
3.3. ESQ Symptoms, TS, HR, and T | REGRESSIONS | Linear regressions revealed that, post-HA, higher TS predicted more severe ESQ symptom scores (A moderate correlation was found between post-trial ESQ symptoms and post-trial HR at baseline ( | PMC9962616 | |
4. Discussion | headache, HT | HEAT, DECAY | The purpose of this study was to examine the changes in environmental stress symptoms (ESQ) pre- and post-trial during HAz, HA, and HT using protocols that incorporate real-life applicable training experiences (intermittently and in both natural and artificial environments). We observed that ESQ symptoms improved significantly over the course of HAz and HA and continued improving when the participants participated in HT twice per week. HT once per week or no HT following HAz and HA was not enough to maintain the ESQ adaptations. We found that the improvements in ESQ symptoms began to return to baseline and even worsen when the participants only participated in HT once per week or not at all following induction. Remarkably, the ESQ is sensitive to the decay of the acclimated/acclimatized state and provides perceptual symptom-related justification for intermittent heat exposure to maintain physiological adaptation. Furthermore, this reflects the sensitivity of the ESQ in determining the optimal dose of heat exposure to facilitate both physiological and perceptual adaptations. This may be important in performance and safety, as it is directly related to how an individual feels and to whether an individual is able to exert their maximal effort during training for optimal benefits or during competitions/missions for maximum performance.Previous researchers have found that the most consistently reported ESQ symptoms in athletes exercising in the heat were “I have a headache”, “I feel dizzy”, “I feel nauseous”, and “I feel hot” [Researchers have also previously found correlations between physiological variables, such as HR and TOur findings that ESQ symptoms track HA, HAz, and HT periods, including breaks and changes in environmental exposure conditions and types, extend the work of the researchers who have found beneficial adaptations in symptoms in isolated single protocols [Our study had a few key limitations directly related to the factors that could influence perceptual symptoms. We aim to study female participants in ongoing and future research. It is possible that other variables that were not controlled for in these analyses, such as sleep and diet, could have impacted ESQ symptoms. However, to minimize the effects of alternative factors, the participants were instructed to practice similar nutritional habits for a period of three days before the trials. Additionally, while the participants were able to utilize multiple modalities of endurance exercise during summer training, including but not limited to running or cycling, HA was limited to treadmill running. Future research could benefit from investigating the responses of the different exercise modalities utilized during HAz and their translation to similar or differing modalities of exercise during HA and HT. Finally, future research should attempt to utilize technology that reports and assesses ESQ symptoms to identify the most feasible and convenient method for the regular monitoring of environmental symptoms during training. | PMC9962616 |
5. Conclusions | HT | HEAT | In conclusion, improvements in ESQ symptoms occurred following HA and were sustained during HT. Our findings suggested that, when assessing ESQ symptoms, multiple variables should be considered and that monitoring should occur after each practice or training session. It is extremely important that ESQ symptoms are monitored frequently over the course of HAz, HA, and HT, as our study exemplified that these symptoms change over this course and that changes in ESQ symptoms can happen more gradually and sensitively to the stimuli. Practically, this is important for coaches to know, as they can easily monitor athletes’ symptoms using the ESQ to track any changes that may occur as athletes adapt to the heat. In the future, it may be more effective and convenient to have technology that can easily be accessed by athletes, allowing them to report ESQ symptoms following training sessions and practices in the heat. Additionally, it may be beneficial for teams that compete in the heat to continue participating in HT at least twice per week following initial pre-season heat exposure. Continued participation in HT twice per week will help facilitate the maintenance of favorable adaptations in environmental stress symptoms throughout the duration of the season. This will be beneficial in preventing environmental stress symptoms from negatively impacting performance when competing in extreme environments. Monitoring ESQ symptoms daily in addition to physiological measures is pertinent to understanding how athletes are adapting to exercise in the heat during initial and repeated exposure to sustain the optimal levels of all adaptations. | PMC9962616 |
Author Contributions | Writing the manuscript—original draft preparation: C.N.M. Data curation: C.N.M., Y.S., C.L.B., C.R.B., M.R.S., R.L.S., L.E.A., E.C.L., and D.J.C. Formal analysis: C.N.M., Y.S., C.L.B., C.R.B., M.R.S., R.L.S., L.E.A., E.C.L., and D.J.C. Investigation: C.N.M., Y.S., C.L.B., C.R.B., M.R.S., R.L.S., L.E.A., E.C.L., and D.J.C. Methodology: C.N.M., Y.S., C.L.B., C.R.B., M.R.S., R.L.S., L.E.A., E.C.L., and D.J.C. Project administration: C.N.M., Y.S., C.L.B., C.R.B., M.R.S., R.L.S., L.E.A., E.C.L., and D.J.C. D.J.C. was the PI of this study. All authors have read and agreed to the published version of the manuscript. | PMC9962616 | ||
Institutional Review Board Statement | This study was approved by the Institutional Review Board at the University of Connecticut (H19-015). | PMC9962616 | ||
Informed Consent Statement | Informed consent, both written and verbal, was given by all the participants in this study. | PMC9962616 | ||
Data Availability Statement | The data from the current study are available in this manuscript. Additional data from the larger study are available at [ | PMC9962616 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC9962616 | ||
References | HT | HEAT | Experimental study design.(Visual representation of relationship between thermal sensation (TS), heart rate (HR), and Environmental Symptoms Questionnaire (ESQ) symptoms during heat acclimatization and heat acclimation. The dotted line indicates line of best fit for HR, and the dashed line indicates the line of best fit for TS.Visual representation of trend in thermal sensation (TS), heart rate (HR), and Environmental Symptoms Questionnaire (ESQ) symptoms over the course of heat training. The dotted line indicates line of best fit for HR, and the dashed line indicates the line of best fit for TS.Means and standard deviations (Ms ± SDs) of post-trial Environmental Symptoms Questionnaire (ESQ) scores at baseline, after heat acclimatization (post-HAz), and after heat acclimation (post-HA). Scores for each item can range from a value of 1 (“not at all”) to 6 (“extreme”).Superscript letters indicate statistical significance of post-ESQ symptoms from baseline to post-HA and from post-HAz to post-HA (Means and standard deviations (Ms ± SDs) of post-trial Environmental Symptoms Questionnaire (ESQ) total scores for maximal heat training group (HT* indicates statistically significant differences ( | PMC9962616 |
Background | We created a clinical virtual reality application for vestibular rehabilitation. Our app targets contextual sensory integration (C.S.I.) where patients are immersed in safe, increasingly challenging environments while practicing various tasks (e.g., turning, walking). The purpose of this pilot study was to establish the feasibility of a randomized controlled trial comparing C.S.I. training to traditional vestibular rehabilitation. | PMC10422780 | ||
Methods | Visual Vertigo, Dizziness, vestibular dysfunction | Thirty patients with vestibular dysfunction completed the Dizziness Handicap Inventory (DHI), Activities-Specific Balance Confidence Scale (ABC), Visual Vertigo Analog Scale (VVAS), Functional Gait Assessment (FGA), Timed-Up-and-Go (TUG), and Four-Square Step Test (FSST). Following initial assessment, the patients were randomized into 8 weeks (once per week in clinic + home exercise program) of traditional vestibular rehabilitation or C.S.I. training. Six patients had to stop participation due to the covid-19 pandemic, 6 dropped out for other reasons (3 from each group). Ten patients in the traditional group and 8 in the C.S.I group completed the study. We applied an intention to treat analysis. | PMC10422780 | |
Results | Following intervention, we observed a significant main effect of time with no main effect of group or group by time interaction for the DHI (mean difference − 18.703, 95% CI [-28.235, -9.172], p = 0.0002), ABC (8.556, [0.938, 16.174], p = 0.028), VVAS, (-13.603, [-25.634, -1.573], p = 0.027) and the FGA (6.405, [4.474, 8.335], p < 0.0001). No changes were observed for TUG and FSST. | PMC10422780 | ||
Conclusion | Patients’ symptoms and function improved following either vestibular rehabilitation method. C.S.I training appeared comparable but not superior to traditional rehabilitation. | PMC10422780 | ||
Trial registration | This study (NCT04268745) was registered on clincaltrials.gov and can be found at | PMC10422780 | ||
Supplementary Information | The online version contains supplementary material available at 10.1186/s12984-023-01224-6. | PMC10422780 | ||
Keywords | PMC10422780 | |||
Introduction | Vestibular disorders, dizziness | VESTIBULAR DISORDERS | Vestibular disorders lead to complaints of dizziness, instability and falls [These days, using virtual reality in a clinical setting to address multisensory integration is becoming increasingly accessible. Head Mounted Displays (HMDs: goggles that are worn on the head in lieu of a screen and projectors), such as the Oculus Rift and the HTC Vive, can potentially allow for a specific and individualized program, with minimal space requirements and a high level of immersion. The theoretical rationale supporting HMD programs for vestibular rehabilitation is clear [To begin answering these questions, we have created a clinical app using the HTC Vive headset to provide contextual sensory integration (C.S.I.) where patients work on their balance while being immersed in safe but increasingly challenging environment [ | PMC10422780 |
Objectives | HMD | Our goal was to create an improved rehabilitation approach using immersive HMD technologies that is individualized to each patient’s functional complaints within the proper context. Low costs and simplicity of use increase the broader impact of this research and the possibility of a large-scale implementation in diverse clinical settings. The purpose of this specific pilot study was to develop the protocol and establish the feasibility of a randomized controlled trial (RCT) comparing C.S.I. training to traditional vestibular rehabilitation. To do that we compared functional and self-reported outcomes between groups before and after the intervention. | PMC10422780 | |
Methods | This study including all covid modifications was approved by the BRANY institutional review board (IRB, # 19-02-223), the IRB at Icahn School of Medicine at Mount Sinai and New York University Committee on activities involving research subjects. | PMC10422780 | ||
Participants | head shaking nystagmus, BPPV, nystagmus, vertigo, dizziness, Dix-Hallpike, Parkinson’s disease, vestibular disorders, Peripheral vestibular hypofunction | NYSTAGMUS, BENIGN PAROXYSMAL POSITIONAL VERTIGO, VESTIBULAR DISORDER | All patients signed consent prior to enrolling in the study. We recruited patients referred to an outpatient vestibular rehabilitation clinic with chief complaints of dizziness and/or imbalance. The participants first underwent an initial vestibular physical therapy evaluation of approximately 60 min. The evaluation included: detailed history, oculomotor screening which included saccades, smooth pursuit and convergence, assessment of spontaneous and gaze evoked nystagmus with and without infrared goggles, Dix-Hallpike and roll test to rule out benign paroxysmal positional vertigo (BPPV), and bedside head impulse test to screen for vestibulo-ocular (VOR) impairment. The assessment also included gait speed and gait stability with head turns in both horizontal planes and vertical planes, as well as the modified clinical test of sensory integration to assess static balance. Peripheral vestibular hypofunction was diagnosed by either positive findings on bithermal caloric testing during videonystagmography and/or positive bedside head impulse test, head shaking nystagmus and gaze evoked nystagmus and/or clinical history characterized by sudden onset of vertigo lasting hours, aural symptoms unilaterally and ruling out other central causes [
Description of Outcome Measures Collected at Baseline and PostIntraclass correlation coefficients of 0.86 and 0.74 were found for interrater and intra-rater reliability of the total FGA scores in vestibular disorders [Internal consistency was 0.79 (no confidence intervals provided for either).The minimal clinically important difference (MCID) on the FGA is considered 4 points [A score lesser or equal to 22 / 30 indicates increased fall risk in community dwelling older adults [Patients are asked to rise up from a chair, walk at their comfortable speed 10 feet, turn around a cone, walk back and sit down.The faster performance out of two trials was recorded.A score slower than 11.1 s in people with vestibular disorders [An MCID of 3.4 s was established for patients post back surgery [A multidirectional stepping test of dynamic balance and coordination. Participants are asked to step over 4 canes on the floor in a clockwise and then counterclockwise direction while being timed.Patients did one practice trial and then we recorded the faster performance out of two trials.A score > 15 s indicates increased fall risk in community dwelling adults over the age of 65 [Whitney et al. identified a cut off score of 12 s for patients with vestibular disorders [A score of less than 67% indicates increased fall risk in community dwelling adults [A minimal detectable change was identified as 13% in patients with Parkinson’s disease [The DHI is classified as mild (under 30), moderate (31–60) or severe (61–100) disability due to dizziness [The MCID for the DHI is considered to be 18 points [ | PMC10422780 |
Procedure | vestibular migraine1 acoustic neuroma | HYPOFUNCTION | When a clinician identified a patient as eligible, they reviewed the informed consent and explained the study procedures. Following consent, patients underwent a baseline assessment including functional measures (Functional Gait Assessment [FGA] [
Description of the C.S.I, Contextual Sensory Integration /Traditional Sample on Pre-treatment VariablesFemale = 18 (60.00%)Male = 12 (40.00%)Female = 9 (60.00%)Male = 6 (40.00%)Female = 9 (60.00%)Male = 6 (40.00%)Abnormal = 12 (40.00%)Normal = 15 (50.00%)NT = 3 (10.00%)Abnormal = 6 (40.00%)Normal = 9 (60.00%)Abnormal = 6 (40.00%)Normal = 6 (40.00%)NT = 3 (20.00%)Abnormal = 11 (36.67%)Normal = 17 (56.67%)NT = 2 (6.67%)Abnormal = 5 (33.33%)Normal = 10 (66.67%)Abnormal = 6 (40.00%)Normal = 7 (46.67%)NT = 2 (13.33%)Abnormal = 8 (26.67%)Normal = 21 (70.00%)NT = 1 (3.33%)Abnormal = 1 (6.67%)Normal = 14 (93.33%)Abnormal = 7 (46.67%)Normal = 7 (46.67%)NT = 1 (6.67%)No = 19 (63.33%)Yes = 11 (36.67%)No = 11 (73.33%)Yes = 4 (26.67%)No = 8 (53.33%)Yes = 7 (46.67%)Normal = 3 (10.00%)Weakness = 16(53.33%)NT = 11 (36.67%)Normal = 2 (13.33%)Weakness = 7 (46.67%)NT = 6 (40.00%)Normal = 1 (6.67%)Weakness: 9 (60.00%)NT = 5 (33.33%)24/30 peripheral hypofunction2 post-concussion2 vestibular migraine2 acoustic neuroma11 peripheral hypofunction1 post-concussion2 vestibular migraine1 acoustic neuroma13 peripheral hypofunction1 post-concussion0 vestibular migraine1 acoustic neuroma#: One way ANOVA, Analysis of Variance; ##: Chi-square for proportions; ###: Kruskal-WallisNT: Not Tested | PMC10422780 |
Randomization and group allocation | We used a blocked randomization method. Instead of randomizing each patient individually, this scheme randomizes several patients at a time in such a way as to ensure that equal numbers are allocated to each group across each segment of time during the length of the study. For example, if the block size is four, we randomize four patients at a time ensuring that two patients are allocated to the C.S.I group and two patients to the traditional group. As it happens, there are six different possible ways we could randomize four patients equally to two treatments. The randomization was done following the baseline assessment and only the study statistician had access to the randomization sequence. | PMC10422780 | ||
Interventions | dizziness | APPENDIX | Following the baseline assessments, we randomized patients to a C.S.I. group or a traditional vestibular rehabilitation control group. We planned each program to be 8 weeks (1 30-minute weekly session + home program). We conducted a post-assessment, identical to the baseline assessment, within one week from the completion of the 8th intervention session.A detailed description of a single patient intervention and home exercise program can be found in appendix A (C.S.I.) and B (Traditional). Below we provide an overview of possible variations. The main difference between participants was the timing of progression which was individualized based on patient symptoms (dizziness and / or instability). Each exercise (in clinic or home) was prescribed at the highest level of challenge that was considered safe (i.e., no loss of balance or no significant increase in dizziness). We assigned all patients a home exercise program (Appendices For patients in the C.S.I. group (Appendix The following are the exercise variation for the traditional group (Appendix | PMC10422780 |
Statistical analysis | We compared the sample before and after shut-down due to covid-19 as well as those who dropped out compared to those who did not using independent sample t-tests for continuous measures that were normally distributed, a Kruskal-Wallis test for skewed continuous variables, and a chi-square test for proportions.To investigate the effect of the intervention, we fit a linear mixed effects model for each outcome measure of interest (FGA, DHI, VVAS, ABC, TUG, FSST) on group (C.S.I. or Traditional), time (pre-, post-intervention) and their interaction. The models also included random intercepts for each participant to account for the inherent correlation between each participant’s performances across the two timepoints. We used sum coding for the categorical predictor variables (group and time) in order to obtain estimates for average differences. Therefore, the coefficient for the factor of time can be interpreted as the average changes in time across both groups. The interaction term between the two variables can be interpreted as any differences observed in one group but not in the other.Because we did not observe any significant differences in the baseline outcome measures between those who participated in the entire study and those who dropped out (see Appendices | PMC10422780 | ||
Results | PMC10422780 | |||
Sample | RECRUITMENT, RECRUITMENT | We began recruitment in September 2019, it was shut down in March 2020 due to the covid-19 pandemic, and resumed in September 2020. See Fig.
Recruitment flow diagram | PMC10422780 | |
Effect of intervention | Dizziness, Visual Vertigo | We observed a significant main effect of time for 4 outcome measures (Fig.
Pre and Post estimated marginal mean and their respective 95% confidence intervals for the traditional and C.S.I groups on outcomes that showed a significant change over time in both groups: The Dizziness Handicap Inventory (DHI pre: Traditional 50.8 [41.1, 60.49], C.S.I 53.07 [43.37, 62.76]. post: Traditional 24 [12.11, 35.89], C.S.I 42.4 [29.15, 55.77]); Functional Gait Analysis (FGA pre: Traditional 21.07 [19.05, 23.08], C.S.I 20.43 [18.34, 22.52]. post: Traditional 27.95 [25.5, 30.41], C.S.I 26.35 [23.61,29.1]); Activities Specific Balance Confidence Scale (ABC pre: Traditional 74.21% [64.39, 84.03], C.S.I 71.34% [61.52, 81.16]. post: Traditional 79.78% [68.41, 91.14], C.S.I 82.89% [70.46, 95.31]); Visual Vertigo Analog Scale (VVAS, pre: Traditional 36.84 [24.72, 48.97], C.S.I 43.05 [30.93, 55.17]. post: Traditional 21.41 [6.51, 36.31], C.S.I 31.28 [14.59, 47.97])
Pre and Post estimated marginal mean and their respective 95% confidence intervals for the for the traditional and C.S.I groups on outcomes that did not change over time in either group: Timed-Up and Go (TUG, pre: Traditional: 7.77 s [6.87, 8.67], C.S.I: 7.97 s [7.02, 8.92], post: Traditional: 8.03 s [7.08, 8.98], C.S.I: 7.93 s [6.88, 8.98]); The Four-Step Square Test (FSST pre: Traditional 9.48 s [7.52, 11.44], C.S.I 11.49 s [9.43, 13.55 ] post: Traditional 9.65 s [7.54, 11.75], C.S.I 10.29 s [7.96, 12.63]) | PMC10422780 | |
Discussion | peripheral hypofunction, visual overload | In this pilot randomized trial patients demonstrated significant, clinically important differences over time in both groups with no evidence of significant differences between groups. Specifically on the FGA both groups began on average at fall risk (below 22) and came closer to maximal score (on average 28 or 26 points out of 30) following the intervention. These changes of a little over 20% are similar to the ones reported in a recent systematic review [C.S.I. is a viable and good intervention option that patients enjoyed with limited symptoms that is not expensive and is feasible in a clinical setting. We designed the app with free graphics that can create sensory load without requiring high computational power [While we did not formally measure enjoyment, most participants were interested in the study because virtual reality was offered and patients in the C.S.I. group appreciated the engagements in salient environments. Greater enjoyment in computerized interventions has been reported before [The two interventions in this study were inherently different, i.e., context specific training (salient balance challenges with dynamic visual load) versus traditional vestibular rehab (gait training and gaze stability exercises), yet the results were remarkably similar. That brings the question of whether the more important aspect to therapy is the home exercise program, including vestibular-specific exercises regardless of method of delivery (i.e. head turning, walking, visual overload). Note that the only difference between the home exercise programs was that the C.S.I group did not perform exercises with eyes closed. It is also possible that research creates a selection bias where patients who agree to be involved in research tend to be more motivated, which could be key to improvement. At the conclusion of our study, the question remains regarding the importance of incorporating different contexts in balance training. More research needs to be done regarding the importance of context in vestibular rehabilitation and what crucial features in virtual reality may help patients the most. In addition, our app allowed for a complete freedom in clinical decision making. The therapists could choose how and when to progress based on patients’ subjective response. Broad implementation of virtual reality technology could allow future studies to incorporate machine learning approaches and potentially design a progression algorithm and test whether such algorithm could enhance patients’ outcomes.We used functional outcome measures that are recommended by the APTA Vestibular Edge Taskforce [Moderate to weak evidence from the clinical practice guidelines for peripheral hypofunction recommend 5–7 weeks of vestibular rehabilitation once a week [Recent CPG guidelines state that clinicians may prescribe static and dynamic balance exercises for a minimum of 20 min for at least 4 to 6 weeks although the strength of the recommendation is weak [ | PMC10422780 | |
Limitations | vestibular migraine, peripheral hypofunction, nystagmus, dizziness, central disorders, HMD, visual vertigo, disability | VIRUS, RECRUITMENT, NYSTAGMUS, DISORDERS, REGRESSION | The study was designed as a small pilot RCT and then became even smaller due to the challenges posed to in-person research in an outpatient setting by the covid-19 pandemic. The high dropout rate (both due to the study closure in March and later associated with challenges in transportation and quarantines), while similar between groups, may have influenced the outcome. While both groups showed significant improvements, it is possible that we were under-powered to detect differences between groups. It is possible for example, that the C.S.I intervention could lead to greater gains in visual vertigo (87.5% of the C.S.I group reported improvement vs. 50% of the traditional) and the traditional intervention in overall disability due to dizziness (62.5% reported some improvement in the C.S.I group vs. 90% of the traditional) but this needs to be investigated in future, larger studies. Functionally, however, the groups showed no trend for differences between them. This was also seen in our companion analysis of head sway data pre and post intervention where the vestibular participants were significantly higher than controls on all outcomes pre rehabilitation. Post rehabilitation they were only significantly higher on sway in mid-frequencies (0.25 to 0.5 Hz) with no indication of any difference between the intervention groups [We did not officially track adherence to the home program and no long-term follow up was conducted. In order to continue the study under covid restrictions including limited personnel, quarantines, difficulty of patients to travel etc., we had to conduct several protocol changes that may influence the internal and external validity of the study. We originally planned to recruit only patients with chronic unilateral peripheral hypofunction. To maximize recruitment post covid we included patients with central disorders as well (2 post-concussion, 2 vestibular migraine) as well as 2 patients (1 in each group) who were considered subacute. While this shows that the C.S.I. intervention is feasible in patients with central disorders, the sample is too small for generalizability. The program was originally planned for 10 weeks: baseline, 8 sessions, post sessions. Due to quarantines and difficulty with travel, some patients took longer to complete, and we often opted to run the assessment on the last day of rehabilitation to mitigate the risk that a patient may not come back due to exposure to the virus. We had originally planned on having a blinded assessor to complete the FGA pre and post but following covid this was no longer feasible, and the treating therapist had to conduct the FGA which may create bias. Note that these modifications influenced both groups in a similar way. While theoretically, all outcomes could be influenced by an assessor, the FGA requires greater clinical judgment than stopwatch-based measures or self-reported outcomes that patients complete on their own. The lack of a blinded outcomes assessor may have influenced the result. There was one variable that significantly differed between groups at baseline: more patients in the C.S.I. group presented with gaze evoked nystagmus which is typically indicative of a more acute lesion. However, these patients were on average equally chronic as the traditional group and so the likelihood of this one different finding influencing the study outcome is low. Lastly, HMD technology continues to develop rapidly, and untethered, high-end headsets are already commercially available at low cost. While this technology carries huge potential for patient-specific sensory integration training in the clinic and possibly at the home, it is important to consider that the hardware was not originally developed for vestibular patients and so development of vestibular-specific applications and rigorous research regarding safety, benefits, progression and regression rules etc. is required to support this potential clinical translation and make sure that only the most effective and safe interventions are disseminated on a large scale. | PMC10422780 |
Conclusions | HMD, vestibular disorders | VESTIBULAR DISORDER | In this pilot randomized clinical trial, patients with vestibular disorders showed clinically important improvements following 8 weeks of vestibular rehabilitation regardless of the intervention approach: traditional vestibular program or contextual sensory integration program via a vestibular-specific HTC Vive application. HMD training within increasingly complex immersive environments appears to be a promising adjunct modality for vestibular rehabilitation but currently does not appear to be superior to other approaches. Our results need to be interpreted with caution because our study is limited by a small and diverse sample. A future larger study with a long-term follow up is required prior to applying these results clinically. | PMC10422780 |
Acknowledgements | The authors wish to acknowledge Prof. Agnieszka Roginska, NYU Steinhardt, for the design of the sounds for the C.S.I app. | PMC10422780 | ||
Authors’ contributions | ZW | RECRUITMENT | JK, AL, DH and MC designed the study and wrote the grant to obtain funding. JK and AL were involved in all aspects of recruitment of participants, data collection, carrying out interventions and drafting and submission of the manuscript. SK, GF, JS and JK recruited participants and performed the intervention programs and evaluations. BM, AM, and SM performed pre and post assessment of participants. MC assisted in recruitment of participants. DH performed data analysis and created tables and figures. ZW and KP developed all software and provided technical support. All authors revised the manuscript. | PMC10422780 |
Funding | Deafness | DISORDERS | This study was funded by the National Institutes of Health National Rehabilitation Research Resource to Enhance Clinical Trials (REACT). Drs. Lubetzky, Harel and Cosetti were funded by an R21DC018101 Early Career Researcher grant from the National Institute on Deafness and Other Communication Disorders (NIDCD). | PMC10422780 |
Data Availability | The datasets for the current study are available from the corresponding author upon request. | PMC10422780 | ||
Declarations | PMC10422780 | |||
Ethics approval and consent to participate | Written informed consent was obtained from all participants before taking part in this study. This study was approved by the BRANY institutional review board (IRB, # 19-02-223), the IRB at Icahn School of Medicine at Mount Sinai and New York University Committee on activities involving research subjects. | PMC10422780 | ||
Consent for publication | Not applicable. | PMC10422780 | ||
Competing interests | The authors declare that they have no competing interests. Prof. Perlin reported a research grant from Unity. Dr. Cosetti reported unpaid participation in research on cochlear implants and other implantable devices manufactured by Cochlear Americas, MED-El, and Oticon Medical. Neither is related to the submitted work. | PMC10422780 | ||
Abbreviations | VERTIGO | Head Mounted DisplayContextual Sensory IntegrationActivities-specific Balance Confidence scaleDizziness Handicap InventoryVisual Vertigo Analog ScaleFunctional Gait AnalysisFour Square Step TestTimed-Up and Go testDynamic Visual AcuityVideo Head Impulse Tests | PMC10422780 | |
References | PMC10422780 | |||
Introduction | asthma | DISEASE, ASTHMA | Academic Editor: Baohui Xu Elevated neutrophil counts in blood, sputum, or lung have been associated with poor clinical outcomes and more severe disease in patients with type 2 asthma. In the phase 3 LIBERTY ASTHMA QUEST ( | PMC10602700 |
Methods | Annualized severe exacerbation rates during the 52-week treatment period and least-squares mean change from baseline in FEV | PMC10602700 | ||
Results | Dupilumab significantly reduced annualized severe exacerbation rates compared with placebo during the 52-week treatment period in patients with elevated type 2 biomarkers, irrespective of baseline neutrophil count ( | PMC10602700 | ||
Conclusions | asthma | ASTHMA | Dupilumab treatment significantly reduced annualized severe exacerbation rates and improved lung function in patients with uncontrolled, moderate-to-severe, type 2 asthma, irrespective of baseline blood neutrophil count. This trial is registered with | PMC10602700 |
1. Introduction | inflammation, non-type, asthma, hypersecretion | INFLAMMATION, ASTHMA, ASTHMA, CHRONIC INFLAMMATORY DISEASE | Asthma is a heterogeneous and chronic inflammatory disease characterized by a spectrum of overlapping phenotypes [Neutrophils may play a key role in asthma, attracting other immune cells and contributing to mucus hypersecretion and increased smooth muscle responsiveness [It has been suggested that non-type 2 mechanisms, including neutrophilic inflammation, may directly affect patient outcomes and the efficacy of asthma treatment [Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for IL-4 and IL-13, key and central drivers of type 2-mediated inflammation [ | PMC10602700 |
2. Methods | PMC10602700 | |||
2.1. Study Design and Patients | Dupilumab, asthma | ASTHMA | QUEST was a phase 3, multicenter, randomized, double-blind, placebo-controlled, parallel-group trial evaluating the safety and efficacy of dupilumab in patients aged ≥12 years with uncontrolled, moderate-to-severe asthma. Dupilumab is approved in the USA as an add-on maintenance treatment in patients with moderate-to-severe asthma aged ≥6 years with an eosinophilic phenotype or with oral corticosteroid-dependent asthma, and in Europe to treat patients with uncontrolled, severe asthma aged ≥6 years [Only patients with type 2 asthma (defined as having baseline blood eosinophils ≥ 150 cells/ | PMC10602700 |
2.2. Endpoints | treatment-emergent adverse events, neutropenia | NEUTROPENIA | Efficacy endpoints assessed in this analysis were annualized severe exacerbation rates over the 52-week treatment period and change from baseline in prebronchodilator FEVSafety was measured in terms of treatment-emergent adverse events (TEAEs) and serious AEs (SAEs) throughout the study. Safety data were evaluated by treatment (dupilumab or placebo) for patients with or without clinically defined treatment-emergent neutropenia (<1,500 cells/ | PMC10602700 |
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