ABSTRACT
Background: Heterogeneous clinical conditions were observed in individuals who had recovered from COVID-19 and some symptoms were found to persist for an extended period post-COVID. Given the non-specific nature of the symptoms, Chinese medicine (CM) is advantageous in providing holistic medical assessment for individuals experiencing persisting problems. Chinese medicine is a type of treatment that involves prescribing regimens based on CM Syndromes diagnosed by CM practitioners. However, inadequate research on CM elements behind the practice has faced scrutiny. Methods: This study analysed 1058 CM medical records from 150 post-COVID-19 individuals via a semi-text-mining approach. A logistic model with MCMCglmm was then utilised to analyse the associations between the indicated factors and identified conditions. Calculations were performed using R Studio and related libraries. Results: With the semi-text-mining approach, three common CM Syndromes (Qi and Yin Deficiency, Lung and Spleen Deficiency, Qi Deficiency of both Spleen and Lung) and nine clinical conditions (fatigue, poor sleep, dry mouth, shortness of breath, cough, headache, tiredness, sweating, coughing phlegm) were identified in the CM clinical records. Analysis via MCMCglmm revealed that the occurrence of persisting clinical conditions was significantly associated with female gender, existing chronic conditions (hypertension, high cholesterol, and diabetes mellitus), and the three persisting CM Syndromes. The current study triangulated the findings from our previous observational study, further showing that patients with certain post-COVID CM Syndromes had significantly increased log-odds of having persisting clinical conditions. Furthermore, this study elucidated that the presence of chronic conditions in the patients would also significantly increase the log-odds of having persistent post-COVID clinical conditions. Conclusion: This study provided insights on mining text-based CM clinical records to identify persistent post-COVID clinical conditions and the factors associated with their occurrence. Future studies could examine the integration of integrating exercise modules, such as health qigong Liuzijue, into multidisciplinary rehabilitation programmes.
ABSTRACT
OBJECTIVES: This study aimed to evaluate the effects of Chinese Medicine (CM) on the health condition of the post-COVID-19 patients, particularly with the CM Syndrome diagnosis and Body Constitutions (BC), as well as related clinical characteristics. METHODS: 150 participants who had COVID-19 and discharged from Hong Kong public hospitals were recruited. They were provided with three to six months of CM treatments, during which assessments were made per month and at follow-up on their CM syndromes, BC, lung functions, and other medical conditions. This study was divided into two parts: (1) Retrospective survey: medical history of participants during COVID-19 hospitalization was collected during the baseline visit; (2) Prospective observation and assessments: clinical symptoms, lung functions, and BC status were evaluated in participants receiving CM treatment based on syndrome differentiation and clinical symptoms. RESULTS: The median hospitalization period was 16 days. Symptoms were presented in 145 (96.6%) patients at the day they were diagnosed with COVID-19. Fever, fatigue, and dry cough were the most common symptoms, exhibiting in 59.3% (89 of 150), 55.3% (83 of 150), and 46% (70 of 150) participants, respectively. Among the 150 post-COVID patients, majority (71.3%) were of the two particular post-COVID CM Syndromes (Qi Deficiency of Lung and Spleen, and Qi and Yin Deficiency). Upon CM treatment, there was an observable increase in participants reaching a balanced BC (i.e. healthy body conditions). The increase was observed to be more prominent in those without the particular CM Syndromes compared to those with the CM Syndromes. Main clinical symptoms in participants with the CM Syndromes decreased upon CM treatment. Occurrence of fatigue also dropped after CM treatment though not all accompanied clinical symptoms were resolved fully. Further to the improvement in terms of CM assessments, lung functions of the participants were found to show improvement after treatment. Both the performance in 6MWT and scores in the LFQ improved upon CM treatments (P < 0.05). CONCLUSION: This study provided evidence for individualized CM treatment on COVID-19 rehabilitation concerning the clinical symptoms improvements, lung functions improvement, and achieving a balanced BC. It is believed that CM may be a key to further promote rehabilitation and resolution of residual symptoms. Long-term large scale follow-up studies on sub-categorising post-COVID patients according to different CM syndromes would be required to further elucidate treatment of persistent symptoms that may be associated with long-COVID.
ABSTRACT
BACKGROUND AND OBJECTIVES: There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. METHODS: This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. RESULTS: The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. CONCLUSION: Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.
Subject(s)
Bayes Theorem , Death , Intensive Care Units , Models, Theoretical , Adult , Female , Humans , Malaysia , Male , Middle Aged , Risk FactorsABSTRACT
INTRODUCTION: Intensive care unit (ICU) prognostic models are predominantly used in more developed nations such as the United States, Europe and Australia. These are not that popular in Southeast Asian countries due to costs and technology considerations. The purpose of this study is to evaluate the suitability of the acute physiology and chronic health evaluation (APACHE) IV model in a single centre Malaysian ICU. MATERIALS AND METHODS: A prospective study was conducted at the single centre ICU in Hospital Sultanah Aminah (HSA) Malaysia. External validation of APACHE IV involved a cohort of 916 patients who were admitted in 2009. Model performance was assessed through its calibration and discrimination abilities. A first-level customisation using logistic regression approach was also applied to improve model calibration. RESULTS: APACHE IV exhibited good discrimination, with an area under receiver operating characteristic (ROC) curve of 0.78. However, the model's overall fit was observed to be poor, as indicated by the Hosmer-Lemeshow goodness-of-fit test (C = 113, P <0.001). Predicted in-ICU mortality rate (28.1%) was significantly higher than the actual in-ICU mortality rate (18.8%). Model calibration was improved after applying first-level customisation (C = 6.39, P = 0.78) although discrimination was not affected. CONCLUSION: APACHE IV is not suitable for application in HSA ICU, without further customisation. The model's lack of fit in the Malaysian study is attributed to differences in the baseline characteristics between HSA ICU and APACHE IV datasets. Other possible factors could be due to differences in clinical practice, quality and services of health care systems between Malaysia and the United States.
