Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195].

[This corrects the article DOI: 10.1016/j.apjon.2023.100195.].

Correction: A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.

[This corrects the article DOI: 10.1371/journal.pone.0296939.].

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?

OBJECTIVE: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations. MATERIALS AND METHODS: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists. The ChatGPT-generated responses were analyzed statistically in a disease-drug matrix, considering disease-medication associations (Q-value) and expert evaluation. RESULTS: ChatGPT achieved a high 98.87% dermatologist approval rate for common dermatological medication recommendations. We evaluated its drug suggestions using the Q-value, showing that human expert validation agreement surpassed Q-value cutoff-based agreement. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. While ChatGPT offered accurate drug advice, it occasionally included incorrect ATC codes, leading to issues like incorrect drug use and type, nonexistent codes, repeated errors, and incomplete medication codes. CONCLUSION: ChatGPT provides medication recommendations as a second opinion in dermatology treatment, but its reliability and comprehensiveness need refinement for greater accuracy. In the future, integrating a medical domain-specific knowledge base for training and ongoing optimization will enhance the precision of ChatGPT's results.

A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.

Call for the responsible artificial intelligence in the healthcare.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI's capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.

Assessment of effects of moon phases on hospital outpatient visits: An observational national study.

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases. Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001-31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records. Results: We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases. Conclusions: The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects.

Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study.

Objective: The popularity of the â€‹"bring your own device (BYOD)" â€‹concept has grown in recent years, and its application has extended to the healthcare field. This study was aimed at examining nurses' acceptance of a BYOD-supported system after a 9-month implementation period. Methods: We used the technology acceptance model to develop and validate a structured questionnaire as a research tool. All nurses (n â€‹= â€‹18) responsible for the BYOD-supported wards during the study period were included in our study. A 5-point Likert scale was used to assess the degree of disagreement and agreement. Statistical analysis was performed in SPSS version 24.0. Results: The questionnaire was determined to be reliable and well constructed, on the basis of the item-level content validity index and Cronbach α values above 0.95 and 0.87, respectively. The mean constant values for all items were above 3.95, thus suggesting that nurses had a positive attitude toward the BYOD-supported system, driven by the characteristics of the tasks involved. Conclusions: We successfully developed a BYOD-supported system. Our study results suggested that nursing staff satisfaction with BYOD-supported systems could be effectively increased by providing practical functionalities and reducing clinical burden. Hospitals could benefit from the insights generated by this study when implementing similar systems.

Effects of mobile health interventions on health-related outcomes in older adults with type 2 diabetes: A systematic review and meta-analysis.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic metabolic condition that is associated with multiple comorbidities. Apart from pharmacological approaches, patient self-management remains the gold standard of care for diabetes. Improving patients' self-management among the elderly with mobile health (mHealth) interventions is critical, especially in times of the COVID-19 pandemic. However, the extent of mHealth efficacy in managing T2DM in the older population remains unknown. Hence, the present review examined the effectiveness of mHealth interventions on cardiometabolic outcomes in older adults with T2DM. METHODS: A systematic search from the inception till May 31, 2021, in the MEDLINE, Embase, and PubMed databases was conducted, and 16 randomized controlled trials were included in the analysis. RESULTS: The results showed significant benefits on glycosylated hemoglobin (HbA1c) (mean difference -0.24%; 95% confidence interval [CI]: -0.44, -0.05; p = 0.01), postprandial blood glucose (-2.91 mmol/L; 95% CI: -4.78, -1.03; p = 0.002), and triglycerides (-0.09 mmol/L; 95% CI: -0.17, -0.02; p = 0.010), but not on low-density lipoprotein cholesterol (-0.06 mmol/L; 95% CI: -0.14, 0.02; p = 0.170), high-density lipoprotein cholesterol (0.05 mmol/L; 95% CI: -0.03, 0.13; p = 0.220), and blood pressure (systolic blood pressure -0.82 mm Hg; 95% CI: -4.65, 3.00; p = 0.670; diastolic blood pressure -1.71 mmHg; 95% CI: -3.71, 0.29; p = 0.090). CONCLUSIONS: Among older adults with T2DM, mHealth interventions were associated with improved cardiometabolic outcomes versus usual care. Its efficacy can be improved in the future as the current stage of mHealth development is at its infancy. Addressing barriers such as technological frustrations may help strategize approaches to further increase the uptake and efficacy of mHealth interventions among older adults with T2DM.

Association between Proton Pump Inhibitor Use and the Risk of Female Cancers: A Nested Case-Control Study of 23 Million Individuals.

Background: Firm conclusions about whether long-term proton pump inhibitor (PPI) drug use impacts female cancer risk remain controversial. Objective: We aimed to investigate the associations between PPI use and female cancer risks. Methods: A nationwide population-based, nested case-control study was conducted within Taiwan's Health and Welfare Data Science Center's databases (2000−2016) and linked to pathologically confirmed cancer data from the Taiwan Cancer Registry (1979−2016). Individuals without any cancer diagnosis during the 17 years of the study served as controls. Case and control patients were matched 1:4 based on age, gender, and visit date. Conditional logistic regression with 95% confidence intervals (CIs) was applied to investigate the association between PPI exposure and female cancer risks by adjusting for potential confounders such as the Charlson comorbidity index and medication usage (metformin, aspirin, and statins). Results: A total of 233,173 female cancer cases were identified, consisting of 135,437 diagnosed with breast cancer, 64,382 with cervical cancer, 19,580 with endometrial cancer, and 13,774 with ovarian cancer. After matching each case with four controls, we included 932,692 control female patients. The number of controls for patients with breast cancer, cervical cancer, endometrial cancer, and ovarian cancer was 541,748, 257,528, 78,320, and 55,096, respectively. The use of PPIs was significantly associated with reduced risk of breast cancer and ovarian cancer in groups aged 20−39 years (adjusted odds ratio (aOR): 0.69, 95%CI: 0.56−0.84; p < 0.001 and aOR: 0.58, 95%CI: 0.34−0.99; p < 0.05, respectively) and 40−64 years (aOR: 0.89, 95%CI: 0.86−0.94; p < 0.0001 and aOR: 0.87, 95%CI: 0.75−0.99; p < 0.05, respectively). PPI exposure was associated with a significant decrease in cervical and endometrial cancer risks in the group aged 40−64 years (with aOR: 0.79, 95%CI: 0.73−0.86; p < 0.0001 and aOR: 0.72, 95%CI: 0.65−0.81; p < 0.0001, respectively). In contrast, in elderly women, PPI use was found to be insignificantly associated with female cancers among users. Conclusions: Our findings, based on real-world big data, can depict a comprehensive overview of PPI usage and female cancer risk. Further clinical studies are needed to elucidate the effects of PPIs on female cancers.

Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort.

Importance: More than 1 billion adults have hypertension globally, of whom 70% cannot achieve their hypertension control goal with monotherapy alone. Data are lacking on clinical use patterns of dual combination therapies prescribed to patients who escalate from monotherapy. Objective: To investigate the most common dual combinations prescribed for treatment escalation in different countries and how treatment use varies by age, sex, and history of cardiovascular disease. Design, Setting, and Participants: This cohort study used data from 11 electronic health record databases that cover 118 million patients across 8 countries and regions between January 2000 and December 2019. Included participants were adult patients (ages ≥18 years) who newly initiated antihypertensive dual combination therapy after escalating from monotherapy. There were 2 databases included for 3 countries: the Iqvia Longitudinal Patient Database (LPD) Australia and Electronic Practice-based Research Network 2019 linked data set from South Western Sydney Local Health District (ePBRN SWSLHD) from Australia, Ajou University School of Medicine (AUSOM) and Kyung Hee University Hospital (KHMC) databases from South Korea, and Khoo Teck Puat Hospital (KTPH) and National University Hospital (NUH) databases from Singapore. Data were analyzed from June 2020 through August 2021. Exposures: Treatment with dual combinations of the 4 most commonly used antihypertensive drug classes (angiotensin-converting enzyme inhibitor [ACEI] or angiotensin receptor blocker [ARB]; calcium channel blocker [CCB]; ß-blocker; and thiazide or thiazide-like diuretic). Main Outcomes and Measures: The proportion of patients receiving each dual combination regimen, overall and by country and demographic subgroup. Results: Among 970 335 patients with hypertension who newly initiated dual combination therapy included in the final analysis, there were 11 494 patients from Australia (including 9291 patients in Australia LPD and 2203 patients in ePBRN SWSLHD), 6980 patients from South Korea (including 6029 patients in Ajou University and 951 patients in KHMC), 2096 patients from Singapore (including 842 patients in KTPH and 1254 patients in NUH), 7008 patients from China, 8544 patients from Taiwan, 103 994 patients from France, 76 082 patients from Italy, and 754 137 patients from the US. The mean (SD) age ranged from 57.6 (14.8) years in China to 67.7 (15.9) years in the Singapore KTPH database, and the proportion of patients by sex ranged from 24 358 (36.9%) women in Italy to 408 964 (54.3%) women in the US. Among 12 dual combinations of antihypertensive drug classes commonly used, there were significant variations in use across country and patient subgroup. For example starting an ACEI or ARB monotherapy followed by a CCB (ie, ACEI or ARB + CCB) was the most commonly prescribed combination in Australia (698 patients in ePBRN SWSLHD [31.7%] and 3842 patients in Australia LPD [41.4%]) and Singapore (216 patients in KTPH [25.7%] and 439 patients in NUH [35.0%]), while in South Korea, CCB + ACEI or ARB (191 patients in KHMC [20.1%] and 1487 patients in Ajou University [24.7%]), CCB + ß-blocker (814 patients in Ajou University [13.5%] and 217 patients in KHMC [22.8%]), and ACEI or ARB + CCB (147 patients in KHMC [15.5%] and 1216 patients in Ajou University [20.2%]) were the 3 most commonly prescribed combinations. The distribution of 12 dual combination therapies were significantly different by age and sex in almost all databases. For example, use of ACEI or ARB + CCB varied from 873 of 3737 patients ages 18 to 64 years (23.4%) to 343 of 2292 patients ages 65 years or older (15.0%) in South Korea's Ajou University database (P for database distribution by age < .001), while use of ACEI or ARB + CCB varied from 2121 of 4718 (44.8%) men to 1721 of 4549 (37.7%) women in Australian LPD (P for drug combination distributions by sex < .001). Conclusions and Relevance: In this study, large variation in the transition between monotherapy and dual combination therapy for hypertension was observed across countries and by demographic group. These findings suggest that future research may be needed to investigate what dual combinations are associated with best outcomes for which patients.

Data Privacy, Regulations and Legal Issues on COVID-19 Tracking Apps: A Scoping Review.

It cannot be deniable that smartphone apps have grown exponentially and are playing a crucial role in the response to the COVID-19 pandemic in many countries. This paper aims to investigate data privacy, regulations and legal issues on COVID-19 tracking apps. A literature search will be followed the PRISMA guidelines extension for a scoping review. The search will be conducted on PubMed and Google Scholar. A total of 38 articles from 7,626 articles were reviewed. Mostly articles report on data privacy. Not many articles report on regulations and legal issues. However, there are many challenges on COVID-19 applications such as security risks, privacy issues, political, ethical, and legal risks, and standardization issues.

Opioid prescribing among new users for non-cancer pain in the USA, Canada, UK, and Taiwan: A population-based cohort study.

BACKGROUND: The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. METHODS AND FINDINGS: We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. CONCLUSIONS: In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic.

An appraisal of healthcare accreditation agencies and programs: similarities, differences, challenges and opportunities.

BACKGROUND: The study, following similar reviews in 2000 and 2010, presents an update of knowledge about external evaluation agencies and accreditation programs. OBJECTIVE: The study aim was to investigate the current profile of external evaluation agencies identifying their program features, and significant changes and challenges.

Digital interventions self-management education for type 1 and 2 diabetes: A systematic review and meta-analysis.

OBJECTIVE: To describe and assess digital health-led diabetes self-management education and support (DSMES) effectiveness in improving glycosylated hemoglobin, diabetes knowledge, and health-related quality of life (HrQoL) of Type 1 and 2 Diabetes in the past 10 years. DESIGN: Systematic Review and Meta-Analysis. The protocol was registered on PROSPERO registration number CRD42019139884. DATA SOURCES: PubMed, EMBASE, Cochrane library, Web of Science, and Scopus between January 2010 and August 2019. Study Selection and Appraisal: Randomized control trials of digital health-led DSMES for Type 1 (T1DM) or 2 (T2DM) diabetes compared to usual care were included. Outcomes were change in HbA1c, diabetes knowledge, and HrQoL. Cochrane Risk of Bias 2.0 tool was used to assess bias and GRADEpro for overall quality. The analysis involved narrative synthesis, subgroup and pooled meta-analyses. RESULTS: From 4286 articles, 39 studies (6861 participants) were included. Mean age was 51.62 years, range (13-70). Meta-analysis revealed intervention effects on HbA1c for T2DM with difference in means (MD) from baseline -0.480% (-0.661, -0.299), I275% (6 months), -0.457% (-0.761, -0.151), I2 81% (12 months), and for T1DM -0.41% (-1.022, 0.208) I2 83% (6 months), -0.03% (-0.210, 0.142) I2 0% (12 months). Few reported HrQoL with Hedges' g 0.183 (-0.039, 0.405), I2 0% (6 months), 0.153 (-0.060, 0.366), I2 0% (12 months) and diabetes knowledge with Hedges' g 1.003 (0.068, 1.938), I2 87% (3 months). CONCLUSION: Digital health-led DSMES are effective in improving HbA1c and diabetes knowledge, notably for T2DM. Research shows non-significant changes in HrQoL. Intervention effect on HbA1c was more impressive if delivered through mobile apps or patient portals. Further research is needed on the impact of DSMES on these outcomes, especially for newly diagnosed diabetes patients.

Prophylactic potential of honey and Nigella sativa L. against hospital and community-based SARS-CoV-2 spread: a structured summary of a study protocol for a randomised controlled trial.

OBJECTIVES: Considering the therapeutic potential of honey and Nigella sativa (HNS) in coronavirus disease 2019 (COVID-19) patients, the objective of the study is defined to evaluate the prophylactic role of HNS. TRIAL DESIGN: The study is a randomized, placebo-controlled, adaptive clinical trial with parallel group design, superiority framework with an allocation ratio of 1:1 among experimental (HNS) and placebo group. An interim analysis will be done when half of the patients have been recruited to evaluate the need to adapt sample size, efficacy, and futility of the trial. PARTICIPANTS: All asymptomatic patients with hospital or community based COVID-19 exposure will be screened if they have had 4 days exposure to a confirmed case. Non-pregnant adults with significant exposure level will be enrolled in the study High-risk exposure (<6 feet distance for >10min without face protection) Moderate exposure (<6 feet distance for >10min with face protection) Subjects with acute or chronic infection, COVID-19 vaccinated, and allergy to HNS will be excluded from the study. Recruitment will be done at Shaikh Zayed Post-Graduate Medical Institute, Ali Clinic and Doctors Lounge in Lahore (Pakistan). INTERVENTION AND COMPARATOR: In this clinical study, patients will receive either raw natural honey (0.5 g) and encapsulated organic Nigella sativa seeds (40 mg) per kg body weight per day or empty capsule with and 30 ml of 5% dextrose water as a placebo for 14 days. Both the natural products will be certified for standardization by Government College University (Botany department). Furthermore, each patient will be given standard care therapy according to version 3.0 of the COVID-19 clinical management guidelines by the Ministry of National Health Services of Pakistan. MAIN OUTCOMES: Primary outcome will be Incidence of COVID-19 cases within 14 days of randomisation. Secondary endpoints include incidence of COVID-19-related symptoms, hospitalizations, and deaths along with the severity of COVID-19-related symptoms till 14th day of randomization. RANDOMISATION: Participants will be randomized into experimental and control groups (1:1 allocation ratio) via the lottery method. There will be stratification based on high risk and moderate risk exposure. BLINDING (MASKING): Quadruple blinding will be ensured for the participants, care providers and outcome accessors. Data analysts will also be blinded to avoid conflict of interest. Site principal investigator will be responsible for ensuring masking. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): 1000 participants will be enrolled in the study with 1:1 allocation. TRIAL STATUS: The final protocol version 1.4 was approved by institutional review board of Shaikh Zayed Post-Graduate Medical Complex on February 15, 2021. The trial recruitment was started on March 05, 2021, with a trial completion date of February 15, 2022. TRIAL REGISTRATION: Clinical trial was registered on February 23, 2021, www.clinicaltrials.gov with registration ID NCT04767087 . FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). With the intention of expediting dissemination of this trial, the conventional formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines.