Impact of Bias in Data Collection of COVID-19 Cases

Homeopathy ◽  
2021 ◽  
Author(s):  
Raj Kumar Manchanda ◽  
Anjali Miglani ◽  
Moumita Chakraborty ◽  
Baljeet Singh Meena ◽  
Kavita Sharma ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Data Collection ◽  
Health Centers ◽  
Open Label ◽  
Randomized Controlled ◽  
Randomized Controlled Studies ◽  
Custom Made ◽  
Homeopathic Remedies ◽  
Gelsemium Sempervirens ◽  
Data Checking

Abstract Background/Objective 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.

scholarly journals Leveraging Data Science for a Personalized Haemodialysis

2020 ◽  
Vol 6 (6) ◽  
pp. 385-394
Author(s):  
Miguel Hueso ◽  
Lluís de Haro ◽  
Jordi Calabia ◽  
Rafael Dal-Ré ◽  
Cristian Tebé ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Kidney Disease ◽  
Personalized Medicine ◽  
Data Collection ◽  
Randomized Controlled Trials ◽  
Data Science ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Use Of Data ◽  
The Right

<b><i>Background:</i></b> The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed. <b><i>Summary:</i></b> Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis. <b><i>Key messages:</i></b> Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

scholarly journals Convalescence plasma treatment of COVID-19: results from a prematurely terminated randomized controlled open-label study in Southern Sweden

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Karin Holm ◽  
Maria N. Lundgren ◽  
Jens Kjeldsen-Kragh ◽  
Oskar Ljungquist ◽  
Blenda Böttiger ◽  
...  
Keyword(s):  
Plasma Treatment ◽  
Viral Infections ◽  
Standard Of Care ◽  
Hospitalized Patients ◽  
Care Group ◽  
Open Label ◽  
Randomized Controlled ◽  
Oxygen Treatment ◽  
Randomized Controlled Studies ◽  
To Receive

Abstract Objective Convalescent plasma has been tried as therapy for various viral infections. Early observational studies of convalescent plasma treatment for hospitalized COVID-19 patients were promising, but randomized controlled studies were lacking at the time. The objective of this study was to investigate if convalescent plasma is beneficial to hospitalized patients with COVID-19. Results Hospitalized patients with confirmed COVID-19 and an oxygen saturation below 94% were randomized 1:1 to receive convalescent plasma in addition to standard of care or standard of care only. The primary outcome was number of days of oxygen treatment to keep saturation above 93% within 28 days from inclusion. The study was prematurely terminated when thirty-one of 100 intended patients had been included. The median time of oxygen treatment among survivors was 11 days (IQR 6–15) for the convalescent plasma group and 7 days (IQR 5–9) for the standard of care group (p = 0.4, median difference -4). Two patients in the convalescent plasma group and three patients in the standard of care group died (p = 0.64, OR 0.49, 95% CI 0.08–2.79). Thus no significant differences were observed between the groups. Trial registration ClinicalTrials NCT04600440, retrospectively registered Oct 23, 2020.

scholarly journals Homeopathic Remedies in COVID-19: Prognostic Factor Research

Homeopathy ◽  
2021 ◽  
Author(s):  
Raj Kumar Manchanda ◽  
Anjali Miglani ◽  
Meeta Gupta ◽  
Baljeet Singh Meena ◽  
Vishal Chadha ◽  
...  
Keyword(s):  
Prognostic Factor ◽  
Public Health Care ◽  
Research Model ◽  
Open Label ◽  
Health Care Clinics ◽  
Course Of Action ◽  
Care Facilities ◽  
Research Findings ◽  
Homeopathic Remedies ◽  
Gelsemium Sempervirens

Abstract 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.

scholarly journals Exploring the Effects of In-App Components on Engagement With a Symptom-Tracking Platform Among Participants With Major Depressive Disorder (RADAR-Engage): Protocol for a 2-Armed Randomized Controlled Trial (Preprint)

2021 ◽  
Author(s):  
Katie M White ◽  
Faith Matcham ◽  
Daniel Leightley ◽  
Ewan Carr ◽  
Pauline Conde ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Major Depressive Disorder ◽  
Clinical Practice ◽  
Data Collection ◽  
Depressive Disorder ◽  
Controlled Trial ◽  
Remote Measurement ◽  
Major Depressive ◽  
Randomized Controlled ◽  
Depression Status

BACKGROUND Multi-parametric remote measurement technologies (RMTs) comprise smartphone apps and wearable devices for both active and passive symptom tracking. They hold potential for understanding current depression status and predicting future depression status. However, the promise of using RMTs for relapse prediction is heavily dependent on user engagement, which is defined as both a behavioral and experiential construct. A better understanding of how to promote engagement in RMT research through various in-app components will aid in providing scalable solutions for future remote research, higher quality results, and applications for implementation in clinical practice. OBJECTIVE The aim of this study is to provide the rationale and protocol for a 2-armed randomized controlled trial to investigate the effect of insightful notifications, progress visualization, and researcher contact details on behavioral and experiential engagement with a multi-parametric mobile health data collection platform, Remote Assessment of Disease and Relapse (RADAR)–base. METHODS We aim to recruit 140 participants upon completion of their participation in the RADAR Major Depressive Disorder study in the London site. Data will be collected using 3 weekly tasks through an active smartphone app, a passive (background) data collection app, and a Fitbit device. Participants will be randomly allocated at a 1:1 ratio to receive either an adapted version of the active app that incorporates insightful notifications, progress visualization, and access to researcher contact details or the active app as usual. Statistical tests will be used to assess the hypotheses that participants using the adapted app will complete a higher percentage of weekly tasks (behavioral engagement: primary outcome) and score higher on self-awareness measures (experiential engagement). RESULTS Recruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022. CONCLUSIONS This study aims to understand how best to promote engagement with RMTs in depression research. The findings will help determine the most effective techniques for implementation in both future rounds of the RADAR Major Depressive Disorder study and, in the long term, clinical practice. CLINICALTRIAL ClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/32653

scholarly journals Current Status of Pain Management in Parkinson’s Disease

2020 ◽  
Vol 47 (3) ◽  
pp. 336-343
Author(s):  
Vikram Karnik ◽  
Nicole Farcy ◽  
Carolina Zamorano ◽  
Veronica Bruno
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Pain Management ◽  
Statistical Significance ◽  
Current Status ◽  
Open Label ◽  
Adequate Treatment ◽  
Pain Thresholds ◽  
Randomized Controlled ◽  
Randomized Controlled Studies

ABSTRACT:Background:Pain is a non-motor symptom in Parkinson’s disease (PD) which commonly goes underreported. Adequate treatment for pain in PD remains challenging, and to date, no clear guidelines for management are available.Methods:With the goal of understanding and organizing the current status of pain management in PD, we conducted a review of pharmacological and non-pharmacological treatments for pain in patients with PD. Suitable studies cataloged in PubMed and the Cochrane database up to October 31, 2019, were included prioritizing randomized controlled trials. Post-hoc analyses and open-label studies were also included.Results:Treatment with levodopa increases pain thresholds in patients with PD. Apomorphine did not have similar efficacy. Duloxetine provided benefit in an open-label trial. Oxycodone-naloxone PR did not have a significant improvement in pain, but per-protocol analysis showed a reduction in pain when adherence was strong. Rotigotine patch had numerical improvement on pain scales with no statistical significance. Safinamide significantly improved the “bodily discomfort” domain in the PDQ-39 questionnaire. Botulinum toxin A had a non-significant signal toward improving dystonic limb pain in PD. DBS to the subthalamic nucleus may modulate central pain thresholds, and a pilot study of cranioelectric therapy warrants future research in the area.Conclusion:After optimizing dopaminergic therapy, understanding the type of pain a patient is experiencing is essential to optimizing pain control in PD. While recommendations can be made regarding the treatment options in each domain, evidence remains weak and future randomized controlled studies are needed.

Relationship between total plasma homocysteine and the risk of aneurysms – a meta-analysis

VASA ◽  
2020 ◽  
pp. 1-6
Author(s):  
Hanji Zhang ◽  
Dexin Yin ◽  
Yue Zhao ◽  
Yezhou Li ◽  
Dejiang Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Single Species ◽  
Total Plasma ◽  
Control Groups ◽  
Randomized Controlled ◽  
Randomized Controlled Studies ◽  
The Difference ◽  
The Relationship ◽  
Healthy Participants

Summary: Our meta-analysis focused on the relationship between homocysteine (Hcy) level and the incidence of aneurysms and looked at the relationship between smoking, hypertension and aneurysms. A systematic literature search of Pubmed, Web of Science, and Embase databases (up to March 31, 2020) resulted in the identification of 19 studies, including 2,629 aneurysm patients and 6,497 healthy participants. Combined analysis of the included studies showed that number of smoking, hypertension and hyperhomocysteinemia (HHcy) in aneurysm patients was higher than that in the control groups, and the total plasma Hcy level in aneurysm patients was also higher. These findings suggest that smoking, hypertension and HHcy may be risk factors for the development and progression of aneurysms. Although the heterogeneity of meta-analysis was significant, it was found that the heterogeneity might come from the difference between race and disease species through subgroup analysis. Large-scale randomized controlled studies of single species and single disease species are needed in the future to supplement the accuracy of the results.

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