Making a Joint Decision Regarding the Timing of Surgery for Elective Arthroplasty Surgery After Being Infected With COVID-19: A Systematic Review.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has caused a substantial number of patients to have their elective arthroplasty surgeries rescheduled. While it is established that patients with COVID-19 who are undergoing surgery have a significantly higher risk of experiencing postoperative complications and mortality, it is not well-known at what time after testing positive the risk of postoperative complications or mortality returns to normal. METHODS: PubMed (MEDLINE), Excerpta Medica dataBASE, and professional society websites were systematically reviewed on March 7, 2022 to identify studies and guidelines on the optimal timeframe to reschedule patients for elective surgery after preoperatively testing positive for COVID-19. Outcomes included postoperative complications such as mortality, pneumonia, acute respiratory distress syndrome, septic shock, and pulmonary embolism. RESULTS: A total of 14 studies and professional society guidelines met the inclusion criteria for this systematic review. Patients with asymptomatic COVID-19 should be rescheduled 4-8 weeks after testing positive (as long as they do not develop symptoms in the interim), patients with mild/moderate COVID-19 should be rescheduled 6-8 weeks after testing positive (with complete resolution of symptoms), and patients with severe/critical COVID-19 should be rescheduled at a minimum of 12 weeks after hospital discharge (with complete resolution of symptoms). CONCLUSIONS: Given the negative association between preoperative COVID-19 and postoperative complications, patients should have elective arthroplasty surgery rescheduled at differing timeframes based on their symptoms. In addition, a multidisciplinary and patient-centered approach to rescheduling patients is recommended. Further study is needed to examine the impact of novel COVID-19 variants and vaccination on timeframes for rescheduling surgery.

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.

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.