Dataset Information

Predicting Domain-Specific Health-Related Quality of Life Using Acute Infarct Volume.

ABSTRACT: BACKGROUND AND PURPOSE:Limited data exist on the relationship between acute infarct volume and health-related quality of life (HRQOL) measures after ischemic stroke. We evaluated whether acute infarct volume predicts domain-specific Neuro-Quality of Life scores at 3 months after stroke. METHODS:Between 2012 and 2014, we prospectively enrolled consecutive patients with ischemic stroke and calculated infarct volume. Outcome scores at 3 months included modified Rankin Scale and Neuro-Quality of Life T scores. We evaluated whether volume organized by quartiles predicted modified Rankin Scale and HRQOL scores at 3 months using logistic and linear regression as appropriate, adjusting for relevant covariates. We calculated variance accounted for (R2) overall and by volume for each domain of HRQOL. RESULTS:Among 490 patients (mean age 64.2±15.86 years; 51.2% male; 63.3% White) included for analysis, 58 (11.8%) were disabled (modified Rankin Scale score of >2) at 3 months. In unadjusted analysis, the highest volume quartile remained a significant predictor of 1 HRQOL domain, applied cognition-general concerns (R2=0.06; P<0.001). Our fully adjusted prediction model explained 32% to 51% of the variance in HRQOL: upper extremity (R2=0.32), lower extremity (R2=0.51), executive function (R2=0.45), and general concerns (R2=0.34). CONCLUSIONS:Acute infarct volume is a poor predictor of HRQOL domains after ischemic stroke, with the exception of the cognitive domain. Overall, clinical and imaging variables explained <50% of the variance in HRQOL outcomes at 3 months. Our data imply that a broad range of factors, some known and others undiscovered, may better predict poststroke HRQOL than what is currently available.

SUBMITTER: Lin C

PROVIDER: S-EPMC5505231 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Predicting Domain-Specific Health-Related Quality of Life Using Acute Infarct Volume.

Lin Chen C Lee Jungwha J Chatterjee Neil N Corado Carlos C Carroll Timothy T Naidech Andrew A Prabhakaran Shyam S

Stroke 20170523 7

<h4>Background and purpose</h4>Limited data exist on the relationship between acute infarct volume and health-related quality of life (HRQOL) measures after ischemic stroke. We evaluated whether acute infarct volume predicts domain-specific Neuro-Quality of Life scores at 3 months after stroke.<h4>Methods</h4>Between 2012 and 2014, we prospectively enrolled consecutive patients with ischemic stroke and calculated infarct volume. Outcome scores at 3 months included modified Rankin Scale and Neuro ...[more]

PMID: 28536175

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Project description:Background Traumatic brain injury (TBI) commonly causes death and disability that can result in productivity loss and economic burden. The health-related quality of life (HRQoL) has been measured in patients suffering from TBI, both in clinical and socioeconomic perspectives. The study aimed to assess the HRQoL in patients following TBI using the European quality of life measure-5 domain-5 level (EQ-5D-5L) questionnaire and develop models for predicting the EQ-5D-5L index score in patients with TBI. Method A cross-sectional study was performed with 193 TBI patients who had completed the EQ-5D-5L questionnaire. The clinical characteristics, Glasgow coma scale (GCS) score, treatment, and Glasgow outcome scale (GOS) were collected. The total data was divided into training data (80%) and testing data (20%); hence, the factors affecting the EQ-5D-5L index scores were used to develop the predictive model with linear and nonlinear regression. The performances of the predictive models were estimated with the adjusted coefficient of determination (R 2 ) and the root mean square error (RMSE). Results A good recovery was found at 96.4%, while 2.1% displayed an unfavorable outcome. Moreover, the mean EQ-5D-5L index scores were 0.91558 (standard deviation [SD] 1.09639). GCS score, pupillary light reflex, surgery, and GOS score significantly correlated with the HRQoL scores. The multiple linear regression model had a high adjusted R 2 of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R 2 of 0.6843 and the lowest RMSE of 0.06748. Conclusions A strong positive correlation between the physician-based outcome as GOS and HRQoL was observed. Furthermore, both the linear and nonlinear regression models were acceptable approaches to predict the HRQoL of patients after TBI. There would be limitations for estimating the HRQoL in unconscious or intubated patients. The HRQoL obtained from the predictive models would be an alternative method to resolve this problem.

Project description:BackgroundPatient-reported outcomes (PRO) allow clinicians to measure health-related quality of life (HRQOL) and understand patients' treatment priorities, but obtaining PRO requires surveys which are not part of routine care. We aimed to develop a preliminary natural language processing (NLP) pipeline to extract HRQOL trajectory based on deep learning models using patient language.Materials and methodsOur data consisted of transcribed interviews of 100 patients undergoing surgical intervention for low-risk thyroid cancer, paired with HRQOL assessments completed during the same visits. Our outcome measure was HRQOL trajectory measured by the SF-12 physical and mental component scores (PCS and MCS), and average THYCA-QoL score.We constructed an NLP pipeline based on BERT, a modern deep language model that captures context semantics, to predict HRQOL trajectory as measured by the above endpoints. We compared this to baseline models using logistic regression and support vector machines trained on bag-of-words representations of transcripts obtained using Linguistic Inquiry and Word Count (LIWC). Finally, given the modest dataset size, we implemented two data augmentation methods to improve performance: first by generating synthetic samples via GPT-2, and second by changing the representation of available data via sequence-by-sequence pairing, which is a novel approach.ResultsA BERT-based deep learning model, with GPT-2 synthetic sample augmentation, demonstrated an area-under-curve of 76.3% in the classification of HRQOL accuracy as measured by PCS, compared to the baseline logistic regression and bag-of-words model, which had an AUC of 59.9%. The sequence-by-sequence pairing method for augmentation had an AUC of 71.2% when used with the BERT model.ConclusionsNLP methods show promise in extracting PRO from unstructured narrative data, and in the future may aid in assessing and forecasting patients' HRQOL in response to medical treatments. Our experiments with optimization methods suggest larger amounts of novel data would further improve performance of the classification model.

Dataset Information

Predicting Domain-Specific Health-Related Quality of Life Using Acute Infarct Volume.

Publications

Predicting Domain-Specific Health-Related Quality of Life Using Acute Infarct Volume.

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