ABSTRACT
The COVID-19 pandemic increased uncertainty about the financial future of many organizations, and regulators alerted auditors to be increasingly skeptical in assessing an entity's ability to continue as a going concern. An auditor's assessment of an entity's ability to continue as a going concern is a matter of significant judgment. This paper proposes to use machine learning to construct a Decision Tree Automated Tool, based on both quantitative financial indicators (e.g., Z-scores) and qualitative factors (e.g., partners' judgment and assessment of industry risk given the pandemic). Considering both quantitative and qualitative factors results in a model that provides additional audit evidence for auditors in their going-concern assessment. An auditing firm in Spain used the model as a supplemental guide, and the model's suggested results were compared to auditors' reports to evaluate its effectiveness and accuracy. The model's predictions were significantly similar to the auditors' assessments, indicating a high level of accuracy, and differences between the model's proposed outcomes and auditors' final conclusions were investigated. This paper also provides insights for regulators on both the use of machine-learning predictive models and additional factors to be considered in future going-concern assessment research. © 2022, Universidad de Huelva. All rights reserved.
ABSTRACT
The COVID-19 pandemic increased uncertainty about the financial future of many organizations, and regulators alerted auditors to be increasingly skeptical in assessing an entity's ability to continue as a going concern. An auditor's assessment of an entity's ability to continue as a going concern is a matter of significant judgment. This paper proposes to use machine learning to construct a Decision Tree Automated Tool, based on both quantitative financial indicators (e.g., Z-scores) and qualitative factors (e.g., partners' judgment and assessment of industry risk given the pandemic). Considering both quantitative and qualitative factors results in a model that provides additional audit evidence for auditors in their going-concern assessment. An auditing firm in Spain used the model as a supplemental guide, and the model's suggested results were compared to auditors' reports to evaluate its effectiveness and accuracy. The model's predictions were significantly similar to the auditors' assessments, indicating a high level of accuracy, and differences between the model's proposed outcomes and auditors' final conclusions were investigated. This paper also provides insights for regulators on both the use of machine-learning predictive models and additional factors to be considered in future going-concern assessment research. © 2022, Universidad de Huelva. All rights reserved.
ABSTRACT
Objective: To evaluate the frequency of infusion-related reactions (IRRs) and PROs following administration of ocrelizumab (OCR) as a 2-hour home infusion. Background: Home-based infusion of multiple sclerosis (MS) drugs may be a safe and convenient treatment during the SARS-CoV-2 pandemic. Design/Methods: 100 MS patients from Rocky Mountains MS Center who fulfill these criteria: 18-55 years;relapsing or primary progressive MS;completed first 600-mg dose of OCR;had neurologist-approved-therapy-monitoring labs;resided in area with 911 access;completed PROs in English;and no >/Grade 3 IRR in prior infusions. Patients completed majority of study visits in home or via telehealth. Primary outcome is IRRs with common terminology criteria for adverse events (CTCAE) collected at the infusion visit, 24 hours post-infusion, and 2 weeks post infusion via telehealth. Patients were asked to compare their home infusion vs last OCR infusion using PROs measuring infusion experience, nurse responsiveness and confidence in receiving a home infusion. Standard statistical methods were used for proportions and change scores. Results: Currently 51/100 patients have received a home infusion. Mean age of 42.5 years (SD +/ - 8.34);73% female;89% white;96% with relapsing MS;mean MS duration 8.8 years;3.3 years on OCR. Only 15.70% (95% CI: (7.02%, 28.59%) experienced an IRR, all classified as Grade 1. CTCAEs were self-reported in 82.35% of patients. Most common by occurrence were fatigue (n=21), itching (n=19), headache/migraine (n=10) and tingling (n=9). No SAEs were reported. These PROs showed improvements pre vs post home infusion (range 1-5, higher is better): nurses explained things clearly (pre=3.78, post=3.94;p=0.01);confidence in nurses administering infusion (pre=4.40, post=4.67;p=0.02);felt safe and respected during infusion (pre=4.45, post=4.69;p=0.03);felt comfortable in surroundings (pre=3.98;post=4.65;p<0.0001);worries about safety and AEs decreased (pre=3.75;post=4.16;p=0.008). Conclusions: Interim analysis of OCR home infusion safety and experience is encouraging.
ABSTRACT
SARS-CoV-2 infection is associated with a high risk of malnutrition, mainly due to increased nutritional requirements and the presence of a severe and universal inflammatory state. Associated symptoms contribute to hyporexia, which perpetuates the negative nutritional balance. Furthermore, dysphagia, especially post-intubation, worsens and makes intake unsafe. This risk is greater in elderly and multimorbid patients. Inflammation to varying degrees is the common link between COVID-19 and the onset of malnutrition, and it is more correct to refer to disease-related malnutrition (DRM). DRM worsens the poor prognosis of SARS-CoV-2 infection, especially in the most severe cases. Therefore, it is necessary to identify and treat people at risk early, avoiding overexposure and direct contact with the patient. We cannot forget the role that a healthy diet plays in both prevention and recovery after discharge.