OBJECTIVE

To describe the treatment outcomes of patients who underwent tube pericardiostomy for all etiologies of non-traumatic massive pericardial effusion or tamponade during the COVID-19 pandemic and determine the association between patient profile and treatment outcomes.

Data were obtained from patients with massive pericardial effusion or cardiac tamponade who underwent surgical drainage from January 1, 2020, to September 1, 2022, in the University of the Philippines – Philippine General Hospital (UP-PGH). These patients’ demographic and clinical profiles, and treatment outcomes were evaluated using frequencies and percentages. Chi-squared and Fisher’s tests determined the differences between COVID (+) and (-) groups. Odds Ratio was used to assess the risk of complications and mortality.

The study population comprised 90 patients with a mean age of 45 years. 54.4% were females. Fifteen (16.67%) were COVID-19 (+) and 75 (83.33%) were COVID-19 (-). Most of the patients were of O+ blood type (34.4%), with no smoking history (67.8%) and no COVID-19 vaccination (76.7%). Common comorbidities were cancer (70%), tuberculosis infection (32.2%), and hypertension (25.6%). No significant difference was found between the two study groups. The presentation was subacute (one week to three months) (62.2%), with the most common symptoms of dyspnea (81.1%), orthopnea (61.1%), and cough (52.2%). Tachycardia (80%) and tachypnea (57.8%) were the most common presenting signs. Hypotension was found more frequently among COVID-19 (+) patients (46.7% vs. 12.0%, p = 0,003, 95% CI). Most patients had abnormal WBC, coagulopathy, elevated inflammatory markers, and cardiac biomarkers. Sinus tachycardia, regular sinus rhythm, ST-T wave changes, and low voltage QRS were common ECG findings. The most common chest X-ray results were pleural effusion (80%), pneumonia (71.1%), and enlarged cardiac border (42.2%). Majority of echocardiographic findings were large effusion (>2 cm) (97.8%), RV collapse (40%), and RA collapse (23.3%). An average of 628 ml of pericardial effusion was drained, predominantly serous and exudative. One specimen yielded a positive AFB culture. 6.7% showed carcinoma cells on fluid cytology. The pericardium was normal in 78.9%. 10.0% of the pericardial biopsy specimen had carcinoma, with metastatic cancer being the most common etiology. The most common cancers were lymphoma (22.7%), breast (25.8%), and lung (16.7%). Hospital length of stay was 18 days in COVID-19 (+) patients and 12 days in COVID (-). The complication and in-hospital mortality rate in the COVID-19 (+) compared to the (-) group (86.7% vs. 73.3% and 46.7% vs. 41.3%, respectively) were not statistically significant. The most common complications were respiratory failure (60%), shock (53.3%), and nosocomial pneumonia (40%). There was no association between clinical factors and the risk for complications. Any complication increased the risk for mortality (OR 15.0, 95% CI 3.2-19.7, p=0.002). The presence of hypertension (OR 0.08, 95% CI 0.02 to 0.4, p=0.001) and subacute duration (OR 0.3, 95% CI 0.09 -0.9, p=0.045) decreased the mortality risk.

Profiles were similar in both groups. There was no association between patient profile and complications. Having COVID-19 did not affect patient outcome. The presence of any complication increases the risk of mortality. In-hospital mortality was high at 42.2%.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods: Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results: In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Objective: To determine the utility of a highly sensitive troponin assay when utilized in the emergency department. Methods The FAST-TRAC study prospectively enrolled >1,500 emergency department patients with suspected acute coronary syndrome within 6 hours of symptom onset and 2 hours of emergency department presentation. It has several unique features that are not found in the majority of studies evaluating troponin. These include a very early presenting population in whom prospective data collection of risk score parameters and the physician’s clinical impression of the probability of acute coronary syndrome before any troponin data were available. Furthermore, two gold standard diagnostic definitions were determined by a pair of cardiologists reviewing two separate data sets; one that included all local troponin testing results and a second that excluded troponin testing so that diagnosis was based solely on clinical grounds. By this method, a statistically valid head-to-head comparison of contemporary and high sensitivity troponin testing is obtainable. Finally, because of a significant delay in sample processing, a unique ability to define the molecular stability of various troponin assays is possible.Trial registration ClinicalTrials.gov Identifier NCT00880802