Test of a Homeopathic Algorithm for COVID-19: the Importance of a Broad Perspective.

BACKGROUND/OBJECTIVE: Most of the symptoms of coronavirus disease 2019 (COVID-19) are covered by large repertory rubrics and hence many remedies have been proposed as "genus epidemicus". The aim of this study was to combine the information from various data collections to prepare a COVID-19 Bayesian mini-repertory/an algorithm-based application (app) and test it. METHODS: In July 2021, 1,161 COVID-19 cases from 100 practitioners globally were combined. These data were used to calculate "condition-confined" likelihood ratios (LRs) for 59 symptoms of COVID-19. Out of these, 35 symptoms of the 11 medicines that had at least 20 cases each were considered. The information was entered in a spreadsheet (algorithm) to calculate combined LRs of specific combinations of symptoms. The algorithm contained the medicines Arsenicum album, Belladonna, Bryonia alba, Camphora, Gelsemium sempervirens, Hepar sulphuris, Mercurius solubilis, Nux vomica, Phosphorus, Pulsatilla and Rhus toxicodendron. To test concordance, the doctors were then invited to re-enter the symptoms of their cases into this algorithm. RESULTS: The algorithm was re-tested on 358 cases, and concordance was seen in 288 cases. On analysis of the data, bias was noticed in the Merc group, which was therefore excluded from the algorithm. The remaining 10 medicines, representing 81.8% of all cases, were included in the preparation of the next version of the homeopathic mini-repertory and app. CONCLUSION: The Bayesian mini-repertory and app is based on qualitative clinical experiences of various doctors in COVID-19 and gives indications for specific medicines for common COVID-19 symptoms. It is freely available [English: https://hpra.co.uk/; Spanish: https://hpra.co.uk/es ] for further testing and utilization by the profession.

Homeopathic Medicines in Second Wave of COVID-19: Prognostic Factor Research.

BACKGROUND/OBJECTIVE: The clinical profile and course of COVID-19 evolved perilously in a second wave, leading to the use of various treatment modalities that included homeopathy. This prognostic factor research (PFR) study aimed to identify clinically useful homeopathic medicines in this second wave. METHODS: This was a retrospective, multi-centred observational study performed from March 2021 to May 2021 on confirmed COVID-19 cases who were either in home isolation or at COVID Care Centres in Delhi, India. The data were collected from integrated COVID Care Centres where homeopathic medicines were prescribed along with conventional treatment. Only those cases that met a set of selection criteria were considered for analysis. The likelihood ratio (LR) was calculated for the frequently occurring symptoms of the prescribed medicines. An LR of 1.3 or greater was considered meaningful. RESULTS: Out of 769 confirmed COVID-19 cases reported, 514 cases were selected for analysis, including 467 in home isolation. The most common complaints were cough, fever, myalgia, sore throat, loss of taste and/or smell, and anxiety. Most cases improved and there was no adverse reaction. Certain new symptoms, e.g., headache, dryness of mouth and conjunctivitis, were also seen. Thirty-nine medicines were prescribed, the most frequent being Bryonia alba followed by Arsenicum album, Pulsatilla nigricans, Belladonna, Gelsemium sempervirens, Hepar sulphuris, Phosphorus, Rhus toxicodendron and Mercurius solubilis. By calculating LR, the prescribing indications of these nine medicines were ascertained. CONCLUSION: Add-on use of homeopathic medicines has shown encouraging results in the second wave of COVID-19 in integrated care facilities. Further COVID-related research is required to be undertaken on the most commonly prescribed medicines.

Generalizability of Homeopathic Prognostic Factor Research Outcome in COVID-19 Treatment: Comparison of Data.

BACKGROUND/OBJECTIVE: During the coronavirus disease 2019 (COVID-19) pandemic, several homeopathic prognostic factor research (PFR) projects have been undertaken. We found two projects with comparable outcomes to assess consistency and possible flaws. METHODS: Two comparisons were made. (1) Outcome of a PFR data collection from the Liga Medicorum Homoeopathica Internationalis (LMHI) by about 100 doctors with 541 cases was compared with a previous analysis of 161 cases in the same database. (2) The updated LMHI database was also compared with a data collection carried out in India by four doctors with a total of 1,445 cases. Differences that resulted in conflicting outcomes (indication in one, contraindication in the other) were examined for possible causes. RESULTS: There was only a single outcome in the updated LMHI database that conflicted with the previous dataset, and this could have been due to statistical variation. The Indian data contained many cases, from few doctors, while the LMHI database had few cases per doctor, but many doctors. The overlap between the projects (individual cases entered in both) was between zero and 22%. In 72 comparisons we found six (8.3%) conflicting outcomes. Possible causes were statistical error due to small numbers of cases and/or observers, confirmation bias, and keynote prescribing if this resulted in symptoms being inadequately checked. CONCLUSION: There was little conflict between the outcomes of the two versions of one project and between the two different PFR projects. Differences could mostly be explained by causes that can be managed. This consistency should primarily be interpreted as showing a strong overall consensus between homeopathic practitioners worldwide, but with variation of consensus between small groups of practitioners.

Impact of Bias in Data Collection of COVID-19 Cases.

BACKGROUND: Prognostic factor research (PFR), prevalence of symptoms and likelihood ratio (LR) play an important role in identifying prescribing indications of useful homeopathic remedies. It involves meticulous unbiased collection and analysis of data collected during clinical practice. This paper is an attempt to identify causes of bias and suggests ways to mitigate them for improving the accuracy in prescribing for better clinical outcomes and execution of randomized controlled studies. METHODS: A prospective, open label, observational study was performed from April 2020 to December 2020 at two COVID Health Centers. A custom-made Excel spreadsheet containing 71 fields covering a spectrum of COVID-19 symptoms was shared with doctors for regular reporting. Cases suitable for PFR were selected. LR was calculated for commonly occurring symptoms. Outlier values with LR ≥5 were identified and variance of LRs was calculated. RESULTS: Out of 1,889 treated cases of confirmed COVID-19, 1,445 cases were selected for pre-specified reasons. Nine medicines, Arsenicum album, Bryonia alba, Gelsemium sempervirens, Pulsatilla nigricans, Hepar sulphuricus, Magnesia muriaticum, Phosphorus, Nux vomica and Belladonna, were most frequently prescribed. Outlier values and large variance for Hepar sulphuricus and Magnesia muriaticum were noticed as indication of bias. Confirmation bias leading to lowering of symptom threshold, keynote prescribing, and deficiency in checking of all symptoms in each case were identified as the most important sources of bias. CONCLUSION: Careful identification of biases and remedial steps such as training of doctors, regular monitoring of data, checking of all pre-defined symptoms, and multicenter data collection are important steps to mitigate biases.

Homeopathic Remedies in COVID-19: Prognostic Factor Research.

BACKGROUND/OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a new disease; its clinical profile and natural history are evolving. Each well-recorded case in homeopathic practice is important for deciding the future course of action. This study aims at identifying clinically useful homeopathic remedies and their prescribing symptoms using the prognostic factor research model. METHODS: This was an open-label, multi-centric, observational study performed from April 2020 to July 2020 at various public health care clinics. The data were collected prospectively from clinical practice at integrated COVID-19 care facilities in India. Good-quality cases were selected using a specific set of criteria. These cases were analyzed for elucidating prognostic factors by calculating the likelihood ratio (LR) of each frequently occurring symptom. The symptoms with high LR values (>1) were considered as prescribing indications of the specific remedy. RESULTS: Out of 327 COVID-19 cases reported, 211 met the selection criteria for analysis. The most common complaints were fatigue, sore throat, dry cough, myalgia, fever, dry mouth and throat, increased thirst, headache, decreased appetite, anxiety, and altered taste. Twenty-seven remedies were prescribed and four of them-Arsenicum album, Bryonia alba, Gelsemium sempervirens, and Pulsatilla nigricans-were the most frequently used. A high LR was obtained for certain symptoms, which enabled differentiation between the remedies for a given patient. CONCLUSION: Homeopathic medicines were associated with improvement in symptoms of COVID-19 cases. Characteristic symptoms of four frequently indicated remedies have been identified using prognostic factor research, findings that can contribute to accurate homeopathic prescribing during future controlled research in COVID-19.

Clinical Characteristics and Remedy Profiles of Patients with COVID-19: A Retrospective Cohort Study.

OBJECTIVE: The aim of the study was to identify indicated homeopathic remedies based on the clinical characteristics of coronavirus disease 2019 (COVID-19) patients in India. METHODS: In this retrospective, cohort study, confirmed COVID-19 patients admitted at a COVID Health Centre in New Delhi between April 29 and June 17, 2020 were given conventional and homeopathic treatment. Patients were grouped into mild, moderate or severe categories of disease. Their symptomatologic profiles were analyzed to identify indicated homeopathic medicines. RESULTS: A total of 196 COVID-19 patients were admitted. One hundred and seventy-eight patients had mild symptoms; eighteen patients had moderate symptoms; no patients with severe symptoms were included as they were referred to tertiary care centers with ventilatory support. The mean age of patients with mild symptoms was significantly lower (38.6 years; standard deviation or SD ± 15.8) compared with patients in the moderate category (66.0 years; SD ± 9.09). The most important symptoms identified were fever (43.4%), cough (47.4%), sore throat (29.6%), headache (18.4%), myalgia (17.9%), fatigue (16.8%), chest discomfort (13.8%), chills (12.6%), shortness of breath (11.2%) and loss of taste (10.2%). Twenty-eight homeopathic medicines were prescribed, the most frequently indicated being Bryonia alba (33.3%), Arsenicum album (18.1%), Pulsatilla nigricans (13.8%), Nux vomica (8%), Rhus toxicodendron (7.2%) and Gelsemium sempervirens (5.8%), in 30C potency. CONCLUSION: Data from the current study reveal that Arsenicum album, Bryonia alba, Pulsatilla nigricans, Nux vomica, Rhus toxicodendron and Gelsemium sempervirens are the most frequently indicated homeopathic medicines. A randomized controlled clinical trial based on this finding is the next step.

Data Collection during the COVID-19 Pandemic: Learning from Experience, Resulting in a Bayesian Repertory.

BACKGROUND: A novel pandemic disease offered the opportunity to create new, disease-specific, symptom rubrics for the homeopathic repertory. OBJECTIVE: The aim of this study was to discover the relationship between specific symptoms and specific medicines, especially of symptoms occurring frequently in this disease. MATERIALS AND METHODS: Worldwide collection of data in all possible formats by various parties was coordinated by the Liga Medicorum Homeopathica Internationalis. As the data came in, more symptoms were assessed prospectively. Frequent analysis and feedback by electronic newsletters were used to improve the quality of the data. Likelihood ratios (LRs) of symptoms were calculated. An algorithm for combining symptom LRs was programmed and published in the form of an app. The app was tested against 18 well-described successful cases from Hong Kong. RESULTS: LRs of common symptoms such as 'Fatigue' and 'Headache' provided better differentiation between medicines than did existing repertory entries, which are based only on the narrow presence or absence of symptoms. A mini-repertory for COVID-19 symptoms was published and supported by a web-based algorithm. With a choice of 20 common symptoms, this algorithm produced the same outcome as a full homeopathic analysis based upon a larger number of symptoms, including some that are traditionally considered more specific to particular medicines. CONCLUSION: A repertory based on clinical data and LRs can differentiate between homeopathic medicines using a limited number of frequently occurring epidemic symptoms. A Bayesian computer algorithm to combine symptoms can complement a full homeopathic analysis of cases.