Study protocol: Traditional Chinese Medicine (TCM) syndrome differentiation for heart failure patients and its implication for long-term therapeutic outcomes of the Qiliqiangxin capsules

Background Syndrome differentiation is a commonly used methodology and practice in Traditional Chinese Medicine (TCM) guiding the diagnosis and treatment of diseases including heart failure (HF). However, previous clinical trials seldom consider the impact of syndrome patterns on the outcome evaluation of TCM formulae. Qiliqiangxin (QLQX) capsule is a TCM formula with cardiotonic effect to improve the cardiovascular function for heart failure with proven efficacy from well-designed clinical trials. Though, there is no clinical trial with a large sample size and long assessment period that considers the relationship between TCM syndrome differentiation and the treatment efficacy of QLQX. In the present study, we design a study protocol to evaluate the relationship between TCM syndrome differentiation and the severity of heart failure as well as its progression. Furthermore, we will evaluate the impact of the TCM syndrome patterns on the efficacy of QLQX in the outcome of heart failure. Methods This is a clinical study conducted in conjunction with an ongoing clinical trial (QUEST Study) by sharing the parent patient populations but with different aims and independent designed roadmaps to investigate the TCM syndrome pattern distributions and the impacts of syndrome pattern types on the efficacy of QLQX in HF treatment. The clinical trial involves over 100 hospitals in mainland China and Hong Kong SAR with 3080 HF patients. By assessing the morbidity and re-hospitalization, we will verify and apply a modified TCM Questionnaire to collect the clinical manifestations of HF and acquire the tongue images of the patients to facilitate the syndrome differentiation. We will base on the “2014 Consensus from TCM experts on diagnosis and treatment of chronic heart failure” to evaluate the TCM syndromes for the patients. A pilot study with at least 600 patients will be conducted to evaluate the reliability, feasibility and validity of the modified TCM questionnaire for syndrome differentiation of HF and the sample size is calculated based on the confidence level of 95%, population size of 3080 and 5% margin of error. Secondly, we will investigate the characteristic of TCM syndrome distribution of HF patients and its correlation with the functional and biochemical data. Furthermore, we will evaluate the relationship between the TCM syndrome patterns and the efficacy of QLQX in the treatment of heart failure. Lastly, we will investigate the implication of tongue diagnosis in the severity and therapeutic outcome of HF. Expect outcomes To our knowledge, this is the first large scale clinical trial to evaluate the impacts of TCM syndrome differentiation on the progression and therapeutic outcome of HF patients and explore the diagnostic value of TCM Tongue Diagnosis in HF patients. We expect to obtain direct clinical evidence to verify the importance of TCM syndrome differentiation for the diagnosis and treatment of HF. Trial Registration: The trial was registered at Chinese Clinical Trial Registry, http://www.chictr.org.cn. (Registration No.: ChiCTR1900021929); Date: 2019-03-16.

Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods

Background Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge. Objective The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms. Methods A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. Results The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. Conclusions The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods. Trial Registration ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908

Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods (Preprint)

BACKGROUND Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge. OBJECTIVE The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms. METHODS A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. RESULTS The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. CONCLUSIONS The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods. CLINICALTRIAL ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908

Model-based reasoning methods for diagnosis in integrative medicine based on electronic medical records and natural language processing

Background: This study aimed to investigate model-based reasoning (MBR) algorithms for the diagnosis of integrative medicine based on electronic medical records (EMRs) and natural language processing. Methods: A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development dataset, and an external test dataset consisting of 1,000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score were used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. Results: The Word2Vec CNN MBR algorithms showed high performance (accuracy of 0.9586 in the test dataset) in the syndrome pattern diagnosis. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test dataset). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. Conclusion: The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis in integrative medicine in lung diseases. The parameters of each group sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods.

An Early Holiday Surprise: Cholecystitis Wrapped in Takotsubo Cardiomyopathy

This is a novel case report of a 44-year-old woman who presented to the emergency department with epigastric pain wrapping around to her back. She had no risk factors for cardiac disease, but her initial electrocardiogram (ECG) showed a Wellens syndrome pattern and she was taken urgently to the catheterization lab. After a negative catheterization, she underwent cardiac magnetic resonance imaging, which was positive for Takotsubo cardiomyopathy (TC). Ultimately, abdominal computed tomography revealed that she had cholecystitis, which likely was the cause of her TC and ECG changes.

Congenital Anomalies Associated with microtia – Anotia: Review of 30 Cases

Background: Microtia-anotia has a global prevalence of 2.6 per 10,000 live births. Children with microtia-anotia will have an associated anomaly or an identifiable syndrome pattern in 20–60% of cases.1 The most common anomalies are facial cleft, facial asymmetry, renal abnormalities, cardiac defects, microphthalmia, polydactyly, and vertebral anomalies. Methods: This series consists of retrospective study of 30 patients who presented to the department of ENT, KEM Hospital between January 2010 to June 2013. Case Records of patients with congenital external ear deformity who presented to the E.N.T. Department during the time period from January 2010 to June 2013 were reviewed for the grade of microtia. The patients were also evaluated for associated congenital anomalies. Results: Associated anomalies in patients included facial paralysis, unilateral renal agenesis, congenital heart diseases, Cleft palate, microphthalmia, microcornea, iris coloboma, hemivertebra. Also Goldenhar syndrome / Hemifacial microsomia, Treacher- Collin syndrome and Pierre- Robin syndrome were the related syndromes to congenital external ear deformity in our series. Conclusion: Congenital external deformities may be associated with spectrum of other anomalies and the patient should receive best coordinated care from otolaryngologists, audiologists, paediatricians, heart specialist to improve the quality of life. Early hearing assessment should be followed by proper rehabilitation for adequate speech and language development. Bangladesh J Otorhinolaryngol; October 2016; 22(2): 78-83