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Objectives: Developing a control group of a clinical trial using real world data is desirable and ethically sound despite challenges pertaining to internal validity. To examine the internal validity, we reproduced the control group in a Randomized Controlled Trial (RCT) using Electric Health Record (EHR) data and evaluated the difference between the outcome of the original trial and that of the reproduced analysis. Method(s): We selected an RCT, REMDACTA trial, that was performed to evaluate the efficacy of tocilizumab plus remdesivir against placebo plus remdesivir in patients with severe COVID-19 pneumonia. We reproduced its control group (patients with severe COVID-19 pneumonia taking only remdesivir), using Japanese EHR data, Millennial Medical Record provided by Life Data Initiative. Target patients for the main analysis were those prescribed remdesivir within 2 days after admission and diagnosed with COVID-19 (defined by ICD-10 code, U07.1) and/or with COVID-19 pneumonia (defined by diagnosis name). Patients in the sub analysis included only those with COVID-19 pneumonia diagnosis. Among the target patients, those undergoing image inspection, oxygen administration, and not taking any medicines for pneumonia before the first remdesivir prescription were eligible for the analyses. Median length of stay was compared in the reproduced group and in REMDACTA trial. Result(s): The database included 676 and 110 target patients for the main and sub analyses, respectively. However, only 57 and 14 patients met the eligibility criteria for the main and sub analyses, respectively. The reduction in the number of patients is attributed to the criteria of image inspection and oxygen administration. Median length of stay in the reproduced group were 13 and 11 days in the main and sub analyses, respectively. In REMDACTA trial, 95% CI of median time was 11.0-16.0. Conclusion(s): We successfully reproduced the median time of the control group by EHR data.Copyright © 2023
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Introduction: Indicators that assess relationships among leukocytes may inform more and/or earlier than those measured in isolation. Method(s): Blood leukocyte differential counts collected from 101 Mayo Clinic COVID-19 patients were related to later outcomes following two approaches: (i) as unstructured data (e.g., lymphocyte percentages) and (ii) as data structures that assess intercellular interactions. Analyzing the same primary data, it was asked whether information contents differed among methods and/or when two sets of structured indicators are used. Result(s): While unstructured data did not distinguish survivors from non-survivors (Fig. 1, rectangle A), one data structure (here identified with letters expressed in italics) exhibited one perpendicular inflection that differentiated two patient groups (B). Two survivor-related observations were also distinguished from the remaining data points (B). A second data structure also revealed a single line of observations and a perpendicular data inflection (C), while more (four) patient groups were identified (D). Four validations were conducted: (i) increasing mortality levels among contiguous data subsets (0, 7.1, 16.2, or 44.4%) suggested construct validity (D);(ii) internal validity was indicated because 22 of the 45 survivors detected by the first data structure were also captured by the second one;(iii) the analysis of patients that differed in address, co-morbidities and other aspects supported external validity;and (iv) quasi non-overlapping data intervals predicted statistical validity (E, F). The structured approach also uncovered new and/ or dissimilar information: different leukocyte-related ratios explained the clusters identified in these analyses (E, F). Conclusion(s): Structured data may yield more information than methods that do not assess multicellular interactions. Possible applications include daily, longitudinal, and personalized analysis of hospital data.
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Background: The COVID-19 pandemic has induced fear and mental health problems in the community and among healthcare workers. Empathy with patients may be difficult in such situations due to urgent conditions. Objective(s): We aimed to evaluate medical students' empathy and fear toward COVID-19 patients during the pandemic. Method(s): This cross-sectional study recruited 107 medical students from Shiraz Medical School in 2021. A Persian version of the Jefferson Scale of Physician Empathy (JSPE) was used to assess the participants' empathy toward COVID-19 patients. The internal validity of the Persian version of JSPE was 0.78, and its test-retest reliability after 14 days was 0.92 in a previous study. The participants were requested to fill out a fear of COVID-19 scale (FCV-19S) previously developed to assess their fear of affliction with COVID-19. Since the normality of data distribution was not approved, we used nonparametric tests, namely, the Mann-Whitney U test and the Spearman correlation coefficient. Result(s): The mean empathy score based on the Persian version JSPE was 71.94 +/- 12.83 out of 140, which was higher in male students and those who resided in dormitories. The mean fear score was 24.93 +/- 6.16 out of 35. Participants living out of dormitories feared COVID-19 to a greater extent. No statistically significant association was found between the age of the participants and these two parameters. The Spearman correlation coefficient test showed that students with a history of COVID-19 had less fear and more empathy because of their experience with COVID-19 (r =-0.249, P-value = 0.02). Conclusion(s): This study highlights the impact of the pandemic on the interaction between medical students as healthcare professionals and patients by affecting medical students' fear and empathy. The study indicates ways to improve readiness for future pandemics. Our study showed that living far away from families in dormitories may influence students' fear and empathy. Moreover, empathy, unlike fear, was affected by gender. A reverse correlation existed between fear and empathy in students with a history of COVID-19, indicating that the more they had empathy, the less they experienced fear.Copyright © 2023, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
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In this paper, the overall efficiency of health centers in Greece is measured by applying the input-oriented model of Data Envelopment Analysis. In addition, four different models were subjected to the input-oriented Data Envelopment Analysis to investigate the contribution of each category of human resources to the efficiency results of the health centers. The bootstrap technique was performed to generate confidence intervals for the models. Data for 155 health centers in Greece were provided by the Ministry of Health. The health centers submitted in the analysis obtained an average efficiency value of 0.932. The average results of the partial models in terms of each input show that the efficiency values achieved by the health centers are mostly influenced by the number of physicians and the number of managers employed. The second factor influencing the efficiency values of the health centers are the number of nursing staff occupied in the health centers. Non-medical staff employed in the health centers had the least contribution to the efficiencies measured. This paper provides important information for the stakeholders and the Government of Greece so as to better allocate the personnel employed in primary health care according to the efficiencies attained by the health centers.
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Efficiency, Organizational , Public Health , Greece , Humans , WorkforceABSTRACT
Issue: Co-enrollment of patients in trials of complex interventions is an emerging phenomenon. Co-enrollment can provide efficiencies, pertinent in the era of a pandemic, but is not without issue. Management of patient co-enrollment, especially in trials investigating multiple interacting components, such as surgical randomized controlled trials, is considered. Setting: IntAct is an international surgical trial which has recruited throughout the COVID pandemic. It is a prospective, parallel group, randomized controlled trial comparing surgery with intraoperative fluorescence angiography against standard care (surgery alone) to determine the effect on anastomotic leak in patients undergoing resection for rectal cancer. Co-enrollment of patients has been requested by nine other trials to date, ranging from interventional drug trials to observational studies. There is minimal published literature regarding co-enrollment to inform discussion and decision-making by the IntAct Trial Management group. Background: Co-enrollment of patients in clinical trials poses a number of potential issues spanning ethical, safety, statistical, and practical concepts, which require careful consideration. Effort has been made to quantify the potential impacts of co-enrollment on statistical power (Myles, 2014). For example, the interaction of treatment effects and substantial or imbalanced co-enrollment have been shown to possess the potential for a large detrimental effect on the sample size. In practice, the nature of such an interaction, or the level of co-enrollment, will not be known in advance. Furthermore, where the intervention is complex in nature, such as in surgical trials of multiple component parts like IntAct, not only is there the possibility of an interaction between the treatment effects of co-enrolling interventions, but also of how the co-enrolling intervention may alter the individual intervention components. There is no published guidance on how to assess these risks a priori. Nonetheless, there are also many potential benefits to allowing co-enrollment. These include increased availability of research opportunities to patients and increased efficiencies to sites when patients contribute to multiple research projects. Disallowing co-enrollment could pose a risk to a trial's recruitment by narrowing the pool of potential participants. Likewise, there is no published guidance on how to assess these benefits a priori. Key considerations for co-enrolling trials: the IntAct Trial Management group decision-making process was informed by discussions regarding: • Internal validity: the ability of the co-enrolling intervention to change the typical operative setting, as well as the post-operative care pathway. • Potential interaction of treatment effects: particularly with regard to the primary endpoint and participant safety. • Generalizability: the possible implications for the trial results. • Recruitment impact: the probable restrictions imposed if co-enrollment was disallowed. • Compliance: the likely effects of co-enrollment on data collection, follow-up visits and withdrawal, and ethical considerations of overburdening participants. We present our rationale and recommendations for guiding Trial Management group discussions when considering and approving co-enrollment of patients in clinical trials. By presenting the experiences of the IntAct international surgical trial Trial Management group, we provide a practical reference for trials considering co-enrollment.