Citations

Background: Procalcitonin (PCT) is recognized as an inflammatory biomarker, often elevated in COVID-19 pneumonia alongside other biomarkers. Understanding its association with severe outcomes and comparing its predictive ability with other biomarkers is crucial for clinical management. Objectives: This retrospective multicenter observational study aimed to investigate the association between PCT levels and adverse outcomes in hospitalized COVID-19 patients. Additionally, it sought to compare the predictive performance of various biomarkers. Design: The study analyzed data from the Society of Critical Care Medicine (SCCM) Viral Infection and Respiratory Illness Universal Study (VIRUS) registry, comprising COVID-19 patients hospitalized across multiple Mayo Clinic sites between March 2020 and June 2022. Methods: A total of 7851 adult COVID-19 patients were included. Patients were categorized into 6 groups based on the worst WHO ordinal scale. Multivariate models were constructed using peak biomarker levels within 72 hours of admission, adjusted for confounders. Results: Elevated PCT levels were independently associated with increased odds of adverse outcomes, including ICU admission (adjusted odds ratio [aOR] 1.32, 95%CI 1.27-1.38), IMV requirement (aOR 1.35, 95%CI: 1.28-1.42), and in-hospital mortality (aOR 1.30, 95%CI: 1.22-1.37). A 3.48-fold increase in IMV requirement and 3.55 times increase in in-hospital mortality were noted with peak PCT ⩾ 0.25 ng/ml. Similar associations were observed with other biomarkers like NLR (AUC 0.730), CRP, IL-6, LDH (AUC 0.800), and D-dimer (AUC 0.719). Models incorporating NLR, LDH, D-dimer, and PCT demonstrated the highest predictive accuracy, with a combined model exhibiting an area under the curve (AUC) of 0.826 (95%CI 0.803-0.849). Conclusions: Higher PCT levels were significantly linked to worse outcomes in COVID-19 patients, emphasizing its potential as a prognostic marker. Biomarker-based predictive models, particularly those including PCT, showed promising utility for risk assessment and clinical decision-making. Further prospective studies are warranted to validate these findings on a larger scale.

Background: The new Global definition of ARDS recently introduced a subgroup known as non-intubated ARDS. This study aimed to assess the risk of progression from noninvasive oxygen support to intubation and ARDS severity based on the S pO 2 /F IO 2 among non-intubated subjects with ARDS. Methods: This retrospective study included subjects with COVID-19 admitted to 7 hospitals (5 in the United States and 2 in Argentina) from January 2020–January 2023. Subjects meeting the new non-intubated ARDS definition (high-flow nasal cannula [HFNC] with an S pO 2 /F IO 2 ≤315 [with S pO 2 ≤97%] or a P aO 2 /F IO 2 ≤300 mm Hg while receiving ≥30 L/min O 2 via HFNC) were included. The study evaluated the proportion of subjects who progressed to intubation, severity levels using the S pO 2 /F IO 2 cutoff proposed in the new ARDS definition, and mortality. Results: Nine hundred sixty-five non-intubated subjects with ARDS were included, of whom 27% ( n = 262) progressed to meet the Berlin criteria within a median of 3 d (interquartile range 2–6). The overall mortality was 23% (95% CI 20–26) ( n = 225), and among subjects who progressed to the Berlin criteria, it was 37% (95% CI 31–43) ( n = 98). Additionally, the worst S pO 2 /F IO 2 within 1 d of ARDS diagnosis was correlated with mortality, with mortality rates of 26% (95% CI 23–30) ( n = 177) for subjects with S pO 2 /F IO 2 ≤148, 17% (95% CI 12–23) ( n = 38) for those with S pO 2 /F IO 2 between 149–234, and 16% (95% CI 8–28) ( n = 10) for subjects maintaining an S pO 2 /F IO 2 higher than 235 ( P < .001). Conclusions: The non-intubated ARDS criteria encompassed a broader spectrum of subjects with lower in-hospital mortality compared to the Berlin criteria. The S pO 2 /F IO 2 and ARDS severity cutoff proposed in the new Global ARDS definition were valuable predictors of in-hospital mortality in these subjects.

The AI-MIRACLE Study investigates the efficacy of using ChatGPT 4.0, a large language model (LLM), for translating and simplifying radiology reports into multiple languages, aimed at enhancing patient comprehension. The study assesses the model’s performance across the most spoken languages in the U.S., emphasizing the accuracy and clarity of translated and simplified radiology reports for non-medical readers. This study employed ChatGPT 4.0 to translate and simplify selected radiology reports into Vietnamese, Tagalog, Spanish, Mandarin, and Arabic. Hindi was used as a preliminary test language for validation of the questionnaire. Performance was assessed via Google form surveys distributed to bilingual physicians, which assessed the translation accuracy and clarity of simplified texts provided by ChatGPT 4. Responses from 24 participants showed mixed results. The study underscores the model’s varying success across different languages, emphasizing both potential applications and limitations. ChatGPT 4.0 shows promise in breaking down language barriers in healthcare settings, enhancing patient comprehension of complex medical information. However, the performance is inconsistent across languages, indicating a need for further refinement and more inclusive training of AI models to handle diverse medical contexts and languages. The study highlights the role of LLMs in improving healthcare communication and patient comprehension, while indicating the need for continued advancements in AI technology, particularly in the translation of low-resource languages.

Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.

Background: Troponin-T (TnT) elevation is common in severe sepsis and septic shock, but the impact on mortality and other clinical outcomes remains uncertain. Methods: This was a retrospective review of adult patients with severe sepsis and septic shock admitted to all the intensive care units (ICU) at Mayo Clinic, Rochester from January 2012 to December 2014. Patients with congenital heart disease or pre-existing moderate or greater valvular disease were excluded. TnT elevation was defined as initial TnT ≥0.1 ng/mL or rise in 3- and 6-hour TnT ≥0.03 ng/mL. Primary outcomes included ICU, hospital, 28-day and 1-year mortality and secondary outcomes were need for and duration of non-invasive ventilation or invasive mechanical ventilation, ICU readmission, and ICU and hospital length of stay. Two-tailed p <0.05 was considered statistically significant. Results: During this three-year period, TnT was measured in 372/765 patients (49%) with septic shock with TnT elevation noted in 329/372 (88%) patients. In patients with and without TnT elevation, initial TnT was 0.14±0.26 vs. 0.14±0.02 ng/mL, p =0.57 and peak TnT was 0.20±0.36 vs. 0.08±0.11 ng/mL, p <0.001. Severity of illness scores, laboratory values, vasopressor use, clinical parameters and medications were similar between patients with and without TnT elevation, except that patients with TnT elevation were older and had more frequent history of coronary atherosclerosis, chronic kidney disease, heart failure and atrial fibrillation (all p <0.05). There was no significant difference in ICU mortality [43 (13%) vs. 8 (19%), p =0.34] and hospital mortality [75 (23%) vs. 12 (28%), p =0.45] between patients with and without TnT elevation. Long-term follow-up of 220/329 (67%) patients showed no significant difference in 28-day mortality [97/216 (45%) vs. 1/4 (25%); p =0.63] or 1-year mortality [163/216 (75%) vs. 4/4 (100%); p =0.58]. There was no significant difference in any of the secondary outcomes between patients with and without elevated TnT (all p >0.05). There was no difference in mortality with increasing quartiles of peak TnT. Conclusions: Troponin elevation is associated with older age and higher comorbidity burden, but does not predict mortality in patients with severe sepsis and septic shock.