Quality of Care in Hospitals and the Use of Mobile-Based Personal Health Record Applications: An Exploratory Study Using National Hospital Evaluation Data.

The use of mobile-based personal health record (m-PHR) applications at the hospital level has been minimally studied. This study aimed to investigate the relationship between m-PHR use and quality of care. A cross-sectional study design was employed, analyzing data from 99 hospitals. Two data sources were utilized: a previous m-PHR investigation conducted from 26 May to 30 June 2022 and a hospital evaluation dataset on quality of care. The use of m-PHR applications was measured by the number of m-PHR application downloads. Three independent variables were assessed: quality of care in the use of antibiotic drugs, injection drugs, and polypharmacy with ≥6 drugs. A generalized linear model was used for the analysis. The hospitals providing high-quality care, as evaluated based on the rate of antibiotic prescription (relative risk [RR], 3.328; 95% confidence interval [CI], 1.840 to 6.020; p < 0.001) and polypharmacy (RR, 2.092; 95% CI, 1.027 to 4.261; p = 0.042), showed an increased number of m-PHR downloads. Among the hospital covariates, public foundation status and being part of multi-hospital systems were associated with the number of m-PHR downloads (p < 0.05). This exploratory study found a positive relationship between quality of care and m-PHR use. Hospitals providing high-quality care may also excel in various activities, including m-PHR application use.

A prediction model for prognosis of nephrotic syndrome with tuberculosis in intensive care unit patients: a nomogram based on the MIMIC-IV v2.2 database.

Background: Currently, a scarcity of prognostic research exists that concentrates on patients with nephrotic syndrome (NS) who also have tuberculosis. The purpose of this study was to assess the in-hospital mortality status of NS patients with tuberculosis, identify crucial risk factors, and create a sturdy prognostic prediction model that can improve disease evaluation and guide clinical decision-making. Methods: We utilized the Medical Information Mart for Intensive Care IV version 2.2 (MIMIC-IV v2.2) database to include 1,063 patients with NS complicated by TB infection. Confounding factors included demographics, vital signs, laboratory indicators, and comorbidities. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and the diagnostic experiment the receiver operating characteristic (ROC) curve analyses were used to select determinant variables. A nomogram was established by using a logistic regression model. The performance of the nomogram was tested and validated using the concordance index (C-index) of the ROC curve, calibration curves, internal cross-validation, and clinical decision curve analysis. Results: The cumulative in-hospital mortality rate for patients with NS and TB was 18.7%. A nomogram was created to predict in-hospital mortality, utilizing Alb, Bun, INR, HR, Abp, Resp., Glu, CVD, Sepsis-3, and AKI stage 7 days. The area under the curve of the receiver operating characteristic evaluation was 0.847 (0.812-0.881), with a calibration curve slope of 1.00 (0.83-1.17) and a mean absolute error of 0.013. The cross-validated C-index was 0.860. The decision curves indicated that the patients benefited from this model when the risk threshold was 0.1 and 0.81. Conclusion: Our clinical prediction model nomogram demonstrated a good predictive ability for in-hospital mortality among patients with NS combined with TB. Therefore, it can aid clinicians in assessing the condition, judging prognosis, and making clinical decisions for such patients.

YouTube as a Source of Patient Information on External Cephalic Version: Cross-Sectional Study.

BACKGROUND: With the global increase of cesarean deliveries, breech presentation is the third indication for elective cesarean delivery. Implementation of external cephalic version (ECV), in which the position of the baby is manipulated externally to prevent breech presentation at term, remains suboptimal. Increasing knowledge for caretakers and patients is beneficial in the uptake of ECV implementation. In recent decades, the internet has become the most important source of information for both patients and health care professionals. However, the use and availability of the internet also bring about concerns since the information is often not regulated or reviewed. Information needs to be understandable, correct, and easily obtainable for the patient. Owing to its global reach, YouTube has great potential to both hinder and support spreading medical information and can therefore be used as a tool for shared decision-making. OBJECTIVE: The objective of this study was to investigate the available information on YouTube about ECV and assess the quality and usefulness of the information in the videos. METHODS: A YouTube search was performed with five search terms and the first 35 results were selected for analysis. A quality assessment scale was developed to quantify the accuracy of medical information of each video. The main outcome measure was the usefulness score, dividing the videos into useful, slightly useful, and not useful categories. The source of upload was divided into five subcategories and two broad categories of medical or nonmedical. Secondary outcomes included audience engagement, misinformation, and encouraging or discouraging ECV. RESULTS: Among the 70 videos, only 14% (n=10) were defined as useful. Every useful video was uploaded by educational channels or health care professionals and 80% (8/10) were derived from a medical source. Over half of the not useful videos were uploaded by birth attendants and vloggers. Videos uploaded by birth attendants scored the highest on audience engagement. The presence of misinformation was low across all groups. Two-thirds of the vloggers encouraged ECV to their viewers. CONCLUSIONS: A minor percentage of videos about ECV on YouTube are considered useful. Vloggers often encourage their audience to opt for ECV. Videos with higher audience engagement had a lower usefulness score compared to videos with lower audience engagement. Sources from medically accurate videos should cooperate with sources with high audience engagement to contribute to the uptake of ECV by creating more awareness and a positive attitude of the procedure, thereby lowering the chance for a cesarean delivery due to breech presentation at term.

Does the Information Quality of ChatGPT Meet the Requirements of Orthopedics and Trauma Surgery?

BACKGROUND: The integration of artificial intelligence (AI) in medicine, particularly through AI-based language models like ChatGPT, offers a promising avenue for enhancing patient education and healthcare delivery. This study aims to evaluate the quality of medical information provided by Chat Generative Pre-trained Transformer (ChatGPT) regarding common orthopedic and trauma surgical procedures, assess its limitations, and explore its potential as a supplementary source for patient education. METHODS: Using the GPT-3.5-Turbo version of ChatGPT, simulated patient information was generated for 20 orthopedic and trauma surgical procedures. The study utilized standardized information forms as a reference for evaluating ChatGPT's responses. The accuracy and quality of the provided information were assessed using a modified DISCERN instrument, and a global medical assessment was conducted to categorize the information's usefulness and reliability. RESULTS: ChatGPT mentioned an average of 47% of relevant keywords across procedures, with a variance in the mention rate between 30.5% and 68.6%. The average modified DISCERN (mDISCERN) score was 2.4 out of 5, indicating a moderate to low quality of information. None of the ChatGPT-generated fact sheets were rated as "very useful," with 45% deemed "somewhat useful," 35% "not useful," and 20% classified as "dangerous." A positive correlation was found between higher mDISCERN scores and better physician ratings, suggesting that information quality directly impacts perceived utility. CONCLUSION: While AI-based language models like ChatGPT hold significant promise for medical education and patient care, the current quality of information provided in the field of orthopedics and trauma surgery is suboptimal. Further development and refinement of AI sources and algorithms are necessary to improve the accuracy and reliability of medical information. This study underscores the need for ongoing research and development in AI applications in healthcare, emphasizing the critical role of accurate, high-quality information in patient education and informed consent processes.

Association between anion gap and the 30-day mortality of patients with ventilator-associated pneumonia: a study of the MIMIC-III database.

Background: Serum anion gap (AG) can potentially be applied to the diagnosis of various metabolic acidosis, and a recent study has reported the association of AG with the mortality of patients with coronavirus disease 2019 (COVID-19). However, the relationship of AG with the short-term mortality of patients with ventilator-associated pneumonia (VAP) is still unclear. Herein, we aimed to investigate the association between AG and the 30-day mortality of VAP patients, and construct and assess a multivariate predictive model for the 30-day mortality risk of VAP. Methods: This retrospective cohort study extracted data of 477 patients with VAP from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Data of patients were divided into a training set and a testing set with a ratio of 7:3. In the training set, variables significantly associated with the 30-day mortality of VAP patients were included in the multivariate predictive model through univariate Cox regression and stepwise regression analyses. Then, the predictive performance of the multivariate predictive model was assessed in both training set and testing set, and compared with the single AG and other scoring systems including the Sequential Organ Failure Assessment (SOFA) score, the confusion, urea, respiratory rate (RR), blood pressure, and age (≥65 years old) (CURB-65) score, and the blood urea nitrogen (BUN), altered mental status, pulse, and age (>65 years old) (BAP-65) score. In addition, the association of AG with the 30-day mortality of VAP patients was explored in subgroups of gender, age, and infection status. The evaluation indexes were hazard ratios (HRs), C-index, and 95% confidence intervals (CIs). Results: A total of 70 patients died within 30 days. The multivariate predictive model consisted of AG (HR =1.052, 95% CI: 1.008-1.098), age (HR =1.037, 95% CI: 1.019-1.055), duration of mechanical ventilation (HR =0.998, 95% CI: 0.996-0.999), and vasopressors use (HR =1.795, 95% CI: 1.066-3.023). In both training set (C-index =0.725, 95% CI: 0.670-0.780) and testing set (C-index =0.717, 95% CI: 0.637-0.797), the multivariate model had a relatively superior predictive performance to the single AG value. Moreover, the association of AG with the 30-day mortality was also found in patients who were male (HR =1.088, 95% CI: 1.029-1.150), and whatever the pathogens they infected (bacterial infection: HR =1.059, 95% CI: 1.011-1.109; fungal infection: HR =1.057, 95% CI: 1.002-1.115). Conclusions: The AG-related multivariate model had a potential predictive value for the 30-day mortality of patients with VAP. These findings may provide some references for further exploration on simple and robust predictors of the short-term mortality risk of VAP, which may further help clinicians to identify patients with high risk of mortality in an early stage in the intensive care units (ICUs).

Clinical significance of the lactate-to-albumin ratio on prognosis in critically ill patients with acute kidney injury.

OBJECTIVE: To explore the relationship between lactate-to-albumin ratio (LAR) at ICU admission and prognosis in critically ill patients with acute kidney injury (AKI). METHODS: A retrospective analysis was conducted. Patients were divided into low (<0.659) LAR and high LAR (≥0.659) groups. Least absolute shrinkage and selection operator regression analysis was conducted to select variables associated with the 30-day prognosis. Cox regression analyses were performed to assess the association between LAR and mortality. Kaplan-Meier curves were plotted to compare cumulative survival rates between high and low LAR groups. Subgroup analysis was employed to assess the stability of the results. ROC curve was used to determine the diagnostic efficacy of LAR on prognosis. RESULTS: A nonlinear relationship was observed between LAR and the risk of 30-day and 360-day all-cause mortality in AKI patients (p < 0.001). Cox regulation showed that high LAR (≥ 0.659) was an independent risk factor for 30-day and 360-day all-cause mortality in patients with AKI (p < 0.001). The Kaplan-Meier survival curves demonstrated a noteworthy decrease in cumulative survival rates at both 30 and 360 days for the high LAR group in comparison to the low LAR group (p < 0.001). Subgroup analyses demonstrated the stability of the results. ROC curves showed that LAR had a diagnostic advantage when compared with lactate or albumin alone (p < 0.001). CONCLUSION: High LAR (≥0.659) at ICU admission was an independent risk factor for both short-term (30-day) and long-term (360-day) all-cause mortality in patients with AKI.

Construction and evaluation of short-term and long-term mortality risk prediction model for patients with sepsis based on MIMIC-IV database. / 基于MIMIC-IVæž„å»ºåŠè¯„ä¼°è„“æ¯’ç—‡æ‚£è€ è¿‘æœŸå’Œè¿œæœŸæ­»äº¡é£Žé™©é¢„æµ‹æ¨¡åž‹.

OBJECTIVES: Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk. METHODS: Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model. RESULTS: A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value. CONCLUSIONS: The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

Utility of Large Language Models for Health Care Professionals and Patients in Navigating Hematopoietic Stem Cell Transplantation: Comparison of the Performance of ChatGPT-3.5, ChatGPT-4, and Bard.

BACKGROUND: Artificial intelligence is increasingly being applied to many workflows. Large language models (LLMs) are publicly accessible platforms trained to understand, interact with, and produce human-readable text; their ability to deliver relevant and reliable information is also of particular interest for the health care providers and the patients. Hematopoietic stem cell transplantation (HSCT) is a complex medical field requiring extensive knowledge, background, and training to practice successfully and can be challenging for the nonspecialist audience to comprehend. OBJECTIVE: We aimed to test the applicability of 3 prominent LLMs, namely ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Bard (Google AI), in guiding nonspecialist health care professionals and advising patients seeking information regarding HSCT. METHODS: We submitted 72 open-ended HSCT-related questions of variable difficulty to the LLMs and rated their responses based on consistency-defined as replicability of the response-response veracity, language comprehensibility, specificity to the topic, and the presence of hallucinations. We then rechallenged the 2 best performing chatbots by resubmitting the most difficult questions and prompting to respond as if communicating with either a health care professional or a patient and to provide verifiable sources of information. Responses were then rerated with the additional criterion of language appropriateness, defined as language adaptation for the intended audience. RESULTS: ChatGPT-4 outperformed both ChatGPT-3.5 and Bard in terms of response consistency (66/72, 92%; 54/72, 75%; and 63/69, 91%, respectively; P=.007), response veracity (58/66, 88%; 40/54, 74%; and 16/63, 25%, respectively; P<.001), and specificity to the topic (60/66, 91%; 43/54, 80%; and 27/63, 43%, respectively; P<.001). Both ChatGPT-4 and ChatGPT-3.5 outperformed Bard in terms of language comprehensibility (64/66, 97%; 53/54, 98%; and 52/63, 83%, respectively; P=.002). All displayed episodes of hallucinations. ChatGPT-3.5 and ChatGPT-4 were then rechallenged with a prompt to adapt their language to the audience and to provide source of information, and responses were rated. ChatGPT-3.5 showed better ability to adapt its language to nonmedical audience than ChatGPT-4 (17/21, 81% and 10/22, 46%, respectively; P=.03); however, both failed to consistently provide correct and up-to-date information resources, reporting either out-of-date materials, incorrect URLs, or unfocused references, making their output not verifiable by the reader. CONCLUSIONS: In conclusion, despite LLMs' potential capability in confronting challenging medical topics such as HSCT, the presence of mistakes and lack of clear references make them not yet appropriate for routine, unsupervised clinical use, or patient counseling. Implementation of LLMs' ability to access and to reference current and updated websites and research papers, as well as development of LLMs trained in specialized domain knowledge data sets, may offer potential solutions for their future clinical application.

How internet healthcare information overflow affects farmers' healthcare consumption: insights from China.

Introduction: In the context of the deep coupling and synergistic development of digital villages and healthy villages, the development of China's rural society harbors a huge potential for medical and healthcare consumption. Methods: On the basis of theoretical research, a framework was constructed to analyze the influence mechanism of farmers' medical and healthcare consumption in the context of Internet medical information overflow, and empirically examines the research and analysis framework by using the 2020 China Household Tracking Survey data with the OLS model, mediation effect model, and instrumental variable method. Results: It is found that Internet medical information spillover has a "crowding-in effect" on farmers' healthcare consumption; Medical attendance behavior, economic capital utilize the intermediary effect between Internet medical information spillover and farmers' healthcare consumption. And there is age group heterogeneity in the effect of Internet medical information spillover on farmers' healthcare consumption, The ability of rural middle-aged and old-aged groups to recognize new things such as Internet medical information needs to be improved, so the overflow of Internet medical information will induce rural middle-aged and old-aged groups to generate a certain amount of medical and health care consumption. However, the impact on healthcare consumption is not sensitive to the youth cohort group. Discussion: The sinking of Internet medical resources should be accelerated in the future to promote the high-quality development of rural medical and health services, at the same time the "Internet + healthcare services" should be optimized to promote scientific and rational stratification of farmers' access to healthcare, and economic capital for farmers' access to health care should be improved in order to alleviate the burden of health care, etc.

Detection Algorithms for Gastrointestinal Perforation Cases in the Medical Information Database Network (MID-NET®) in Japan.

BACKGROUND: The Medical Information Database Network (MID-NET®) in Japan is a vast repository providing an essential pharmacovigilance tool. Gastrointestinal perforation (GIP) is a critical adverse drug event, yet no well-established GIP identification algorithm exists in MID-NET®. METHODS: This study evaluated 12 identification algorithms by combining ICD-10 codes with GIP therapeutic procedures. Two sites contributed 200 inpatients with GIP-suggestive ICD-10 codes (100 inpatients each), while a third site contributed 165 inpatients with GIP-suggestive ICD-10 codes and antimicrobial prescriptions. The positive predictive values (PPVs) of the algorithms were determined, and the relative sensitivity (rSn) among the 165 inpatients at the third institution was evaluated. RESULTS: A trade-off between PPV and rSn was observed. For instance, ICD-10 code-based definitions yielded PPVs of 59.5%, whereas ICD-10 codes with CT scan and antimicrobial information gave PPVs of 56.0% and an rSn of 97.0%, and ICD-10 codes with CT scan and antimicrobial information as well as three types of operation codes produced PPVs of 84.2% and an rSn of 24.2%. The same algorithms produced statistically significant differences in PPVs among the three institutions. Combining diagnostic and procedure codes improved the PPVs. The algorithm combining ICD-10 codes with CT scan and antimicrobial information and 80 different operation codes offered the optimal balance (PPV: 61.6%, rSn: 92.4%). CONCLUSION: This study developed valuable GIP identification algorithms for MID-NET®, revealing the trade-offs between accuracy and sensitivity. The algorithm with the most reasonable balance was determined. These findings enhance pharmacovigilance efforts and facilitate further research to optimize adverse event detection algorithms.

Hypocapnia is associated with increased in-hospital mortality and 1 year mortality in acute heart failure patients.

AIMS: Both hypercapnia and hypocapnia are common in patients with acute heart failure (AHF), but the association between partial pressure of arterial carbon dioxide (PaCO2) and AHF prognosis remains unclear. The objective of this study was to investigate the connection between PaCO2 within 24 h after admission to the intensive care unit (ICU) and mortality during hospitalization and at 1 year in AHF patients. METHODS AND RESULTS: AHF patients were enrolled from the Medical Information Mart for Intensive Care IV database. The patients were divided into three groups by PaCO2 values of <35, 35-45, and >45 mmHg. The primary outcome was to investigate the connection between PaCO2 and in-hospital mortality and 1 year mortality in AHF patients. The secondary outcome was to assess the prediction value of PaCO2 in predicting in-hospital mortality and 1 year mortality in AHF patients. A total of 2374 patients were included in this study, including 457 patients in the PaCO2 < 35 mmHg group, 1072 patients in the PaCO2 = 35-45 mmHg group, and 845 patients in the PaCO2 > 45 mmHg group. The in-hospital mortality was 19.5%, and the 1 year mortality was 23.9% in the PaCO2 < 35 mmHg group. Multivariate logistic regression analysis showed that the PaCO2 < 35 mmHg group was associated with an increased risk of in-hospital mortality [hazard ratio (HR) 1.398, 95% confidence interval (CI) 1.039-1.882, P = 0.027] and 1 year mortality (HR 1.327, 95% CI 1.020-1.728, P = 0.035) than the PaCO2 = 35-45 mmHg group. The PaCO2 > 45 mmHg group was associated with an increased risk of in-hospital mortality (HR 1.387, 95% CI 1.050-1.832, P = 0.021); the 1 year mortality showed no significant difference (HR 1.286, 95% CI 0.995-1.662, P = 0.055) compared with the PaCO2 = 35-45 mmHg group. The Kaplan-Meier survival curves showed that the PaCO2 < 35 mmHg group had a significantly lower 1 year survival rate. The area under the receiver operating characteristic curve for predicting in-hospital mortality was 0.591 (95% CI 0.526-0.656), and the 1 year mortality was 0.566 (95% CI 0.505-0.627) in the PaCO2 < 35 mmHg group. CONCLUSIONS: In AHF patients, hypocapnia within 24 h after admission to the ICU was associated with increased in-hospital mortality and 1 year mortality. However, the increase in 1 year mortality may be influenced by hospitalization mortality. Hypercapnia was associated with increased in-hospital mortality.

Association between triglyceride glucose and acute kidney injury in patients with acute myocardial infarction: a propensity score­matched analysis.

BACKGROUND: Acute kidney injury (AKI) in patients with acute myocardial infarction (AMI) often indicates a poor prognosis. OBJECTIVE: This study aimed to investigate the association between the TyG index and the risk of AKI in patients with AMI. METHODS: Data were taken from the Medical Information Mart for Intensive Care (MIMIC) database. A 1:3 propensity score (PS) was set to match patients in the AKI and non-AKI groups. Multivariate logistic regression analysis, restricted cubic spline (RCS) regression and subgroup analysis were performed to assess the association between TyG index and AKI. RESULTS: Totally, 1831 AMI patients were included, of which 302 (15.6%) had AKI. The TyG level was higher in AKI patients than in non-AKI patients (9.30 ± 0.71 mg/mL vs. 9.03 ± 0.73 mg/mL, P < 0.001). Compared to the lowest quartile of TyG levels, quartiles 3 or 4 had a higher risk of AKI, respectively (Odds Ratiomodel 4 = 2.139, 95% Confidence Interval: 1.382-3.310, for quartile 4 vs. quartile 1, Ptrend < 0.001). The risk of AKI increased by 34.4% when the TyG level increased by 1 S.D. (OR: 1.344, 95% CI: 1.150-1.570, P < 0.001). The TyG level was non-linearly associated with the risk of AKI in the population within a specified range. After 1:3 propensity score matching, the results were similar and the TyG level remained a risk factor for AKI in patients with AMI. CONCLUSION: High levels of TyG increase the risk of AKI in AMI patients. The TyG level is a predictor of AKI risk in AMI patients, and can be used for clinical management.

Effect of Histamine H2 Receptor Antagonists on All-Cause Mortality in Critically Ill Patients With Essential Hypertension: A Retrospective Cohort Study.

Previous studies found that histamine H2 receptor antagonists (H2RAs) had blood pressure lowering and cardioprotective effects, but the impact of H2RAs on the survival outcomes of critically ill patients with essential hypertension is still unclear. The aim of this study was to investigate the association of H2RAs exposure with all-cause mortality in patients with essential hypertension based on Medical Information Mart for Intensive Care III database. A total of 17,739 patients were included, involving 8482 H2RAs users and 9257 non-H2RAs users. Propensity score matching (PSM) was performed to improve balance between 2 groups that were exposed to H2RAs or not. Kaplan-Meier survival curves were used to compare the cumulative survival rates and multivariable Cox regression models were performed to evaluate the association between H2RAs exposure and all-cause mortality. After 1:1 PSM, 4416 pairs of patients were enrolled. The results revealed potentially significant association between H2RAs exposure and decreased 30-day, 90-day, and 1-year mortalities in multivariate analyses (HR = 0.783, 95% CI: 0.696-0.882 for 30-day; HR = 0.860, 95% CI: 0.778-0.950 for 90-day; and HR = 0.883, 95% CI: 0.811-0.961 for 1-year mortality, respectively). Covariate effect analyses showed that the use of H2RAs was more beneficial in essential hypertension patients with age ≥ 60, BMI ≥ 25 kg/m2, coronary arteriosclerosis, stroke, and acute kidney failure, respectively. In conclusion, H2RAs exposure was related to lower mortalities in critically ill patients with essential hypertension, which provided novel potential strategy for the use of H2RAs in essential hypertension patients.

Association between statin use and acute pulmonary embolism in intensive care unit patients with sepsis: a retrospective cohort study.

Introduction: Acute pulmonary embolism (APE) is a life-threatening medical condition that is frequently encountered and associated with significant incidence and mortality rates, posing a substantial threat to patients' well-being and quality of life. Sepsis is prominent independent risk factor for the development of APE. Despite recent investigations indicating a reduced APE risk through statin therapy, its impact on patients with sepsis and APE remains unresolved. Methods: The Medical Information Mart for Intensive Care (MIMIC)-IV database was utilized to identify patients diagnosed with sepsis and APE, irrespective of statin treatment status, as part of this study. The primary study aim was to assess the risk of APE, which was analyzed using multivariate logistic regression models. Results: The study encompassed a total of 16,633 participants, with an average age of 64.8 ± 16.2 years. Multivariate logistic regression revealed that septic patients receiving statin therapy in the intensive care unit (ICU) exhibited a 33% reduction in the risk of developing APE (OR = 0.67, 95% CI: 0.52-0.86, p < 0.001). The findings of further analyses, including stratification based on statin usage, dosage, and propensity score matching, consistently reinforced the hypothesis that administering statins to patients with sepsis effectively mitigates their potential APE risk. Discussion: The results of the study provide compelling evidence in favor of administering statins to septic patients as a prophylactic measure against APE, given that statins may reduce the risk of developing APE, and their anti-APE effect appears to be dose-dependent. Nonetheless, future randomized controlled trials are needed to validate these results.

The effect of electronic medical records on medication errors, workload, and medical information availability among qualified nurses in Israel- a cross sectional study.

BACKGROUND: Errors in medication administration by qualified nursing staff in hospitals are a significant risk factor for patient safety. In recent decades, electronic medical records (EMR) systems have been implemented in hospitals, and it has been claimed that they contribute to reducing such errors. However, systematic research on the subject in Israel is scarce. This study examines the position of the qualified nursing staff regarding the impact of electronic medical records systems on factors related to patient safety, including errors in medication administration, workload, and availability of medical information. METHODS: This cross-sectional study examines three main variables: Medication errors, workload, and medical information availability, comparing two periods- before and after EMR implementation based on self-reports. A final sample of 591 Israeli nurses was recruited using online private social media groups to complete an online structured questionnaire. The questionnaires included items assessing workload (using the Expanding Nursing Stress Scale), medical information availability (the Carrington-Gephart Unintended Consequences of Electronic Health Record Questionnaire), and medical errors (the Medical Error Checklists). Items were assessed twice, once for the period before the introduction of electronic records and once after. In addition, participants answered open-ended questions that were qualitatively analyzed. RESULTS: Nurses perceive the EMR as reducing the extent of errors in drug administration (mean difference = -0.92 ± 0.90SD, p < 0.001), as well as the workload (mean difference = -0.83 ± 1.03SD, p < 0.001) by ∼ 30% on average, each. Concurrently, the systems are perceived to require a longer documentation time at the expense of patients' treatment time, and they may impair the availability of medical information by about 10% on average. CONCLUSION: The results point to nurses' perceived importance of EMR systems in reducing medication errors and relieving the workload. Despite the overall positive attitudes toward EMR systems, nurses also report that they reduce information availability compared to the previous pen-and-paper approach. A need arises to improve the systems in terms of planning and adaptation to the field and provide appropriate technical and educational support to nurses using them.

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study.

BACKGROUND: Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies. OBJECTIVE: This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs). METHODS: We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families. RESULTS: A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope. CONCLUSIONS: This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

Dr. Google to Dr. ChatGPT: assessing the content and quality of artificial intelligence-generated medical information on appendicitis.

INTRODUCTION: Generative artificial intelligence (AI) chatbots have recently been posited as potential sources of online medical information for patients making medical decisions. Existing online patient-oriented medical information has repeatedly been shown to be of variable quality and difficult readability. Therefore, we sought to evaluate the content and quality of AI-generated medical information on acute appendicitis. METHODS: A modified DISCERN assessment tool, comprising 16 distinct criteria each scored on a 5-point Likert scale (score range 16-80), was used to assess AI-generated content. Readability was determined using the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) scores. Four popular chatbots, ChatGPT-3.5 and ChatGPT-4, Bard, and Claude-2, were prompted to generate medical information about appendicitis. Three investigators independently scored the generated texts blinded to the identity of the AI platforms. RESULTS: ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 had overall mean (SD) quality scores of 60.7 (1.2), 62.0 (1.0), 62.3 (1.2), and 51.3 (2.3), respectively, on a scale of 16-80. Inter-rater reliability was 0.81, 0.75, 0.81, and 0.72, respectively, indicating substantial agreement. Claude-2 demonstrated a significantly lower mean quality score compared to ChatGPT-4 (p = 0.001), ChatGPT-3.5 (p = 0.005), and Bard (p = 0.001). Bard was the only AI platform that listed verifiable sources, while Claude-2 provided fabricated sources. All chatbots except for Claude-2 advised readers to consult a physician if experiencing symptoms. Regarding readability, FKGL and FRE scores of ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 were 14.6 and 23.8, 11.9 and 33.9, 8.6 and 52.8, 11.0 and 36.6, respectively, indicating difficulty readability at a college reading skill level. CONCLUSION: AI-generated medical information on appendicitis scored favorably upon quality assessment, but most either fabricated sources or did not provide any altogether. Additionally, overall readability far exceeded recommended levels for the public. Generative AI platforms demonstrate measured potential for patient education and engagement about appendicitis.

Association Between the Red Blood Cell Distribution Width and 30-Day Mortality in Intensive Care Patients Undergoing Cardiac Surgery: A Retrospective Observational Study Based on the Medical Information Mart for Intensive Care-IV Database.

Background: Millions of patients undergo cardiac surgery each year. The red blood cell distribution width (RDW) could help predict the prognosis of patients who undergo percutaneous coronary intervention or coronary artery bypass surgery. We investigated whether the RDW has robust predictive value for the 30-day mortality among patients in an intensive care unit (ICU) after undergoing cardiac surgery. Methods: Using the Medical Information Mart for Intensive Care-IV Database, we retrieved data for 11,634 patients who underwent cardiac surgery in an ICU. We performed multivariate Cox regression analysis to model the association between the RDW and 30-day mortality and plotted Kaplan-Meier curves. Subgroup analyses were stratified using relevant covariates. Receiver operating characteristic (ROC) curves were used to determine the predictive value of the RDWs. Results: The total 30-day mortality rate was 4.2% (485/11,502). The elevated-RDW group had a higher 30-day mortality rate than the normal-RDW group (P&0.001). The robustness of our data analysis was confirmed by performing subgroup analyses. Each unit increase in the RDW was associated with a 17% increase in 30-day mortality when the RDW was used as a continuous variable (adjusted hazard ratio=1.17, 95% confidence interval, 1.10-1.25). Our ROC results showed the predictive value of the RDW. Conclusions: An elevated RDW was associated with a higher 30-day mortality in patients after undergoing cardiac surgery in an ICU setting. The RDW can serve as an efficient and accessible method for predicting the mortality of patients in ICUs following cardiac surgery.

Arabic Web-Based Information on Oral Lichen Planus: Content Analysis.

BACKGROUND: The use of web-based health information (WBHI) is on the rise, serving as a valuable tool for educating the public about health concerns and enhancing treatment adherence. Consequently, evaluating the availability and quality of context-specific WBHI is crucial to tackle disparities in health literacy and advance population health outcomes. OBJECTIVE: This study aims to explore and assess the quality of the WBHI available and accessible to the public on oral lichen planus (OLP) in Arabic. METHODS: The Arabic translation of the term OLP and its derivatives were searched in three general search platforms, and each platform's first few hundred results were reviewed for inclusion. We excluded content related to cutaneous LP, content not readily accessible to the public (eg, requiring subscription fees or directed to health care providers), and content not created by health care providers or organizations (ie, community forums, blogs, and social media). We assessed the quality of the Arabic WBHI with three standardized and validated tools: DISCERN, Journal of the American Medical Association (JAMA) benchmarks, and Health On the Net (HON). RESULTS: Of the 911 resources of WBHI reviewed for eligibility, 49 were included in this study. Most WBHI resources were provided by commercial affiliations (n=28, 57.1%), with the remainder from academic or not-for-profit affiliations. WBHI were often presented with visual aids (ie, images; n=33, 67.4%). DISCERN scores were highest for WBHI resources that explicitly stated their aim, while the lowest scores were for providing the effect of OLP (or OLP treatment) on the quality of life. One-quarter of the resources (n=11, 22.4%) met all 4 JAMA benchmarks, indicating the high quality of the WBHI, while the remainder of the WBHI failed to meet one or more of the JAMA benchmarks. HON scores showed that one-third of WBHI sources had scores above 75%, indicating higher reliability and credibility of the WBHI source, while one-fifth of the sources scored below 50%. Only 1 in 7 WBHI resources scored simultaneously high on all three quality instruments. Generally, WBHI from academic affiliations had higher quality scores than content provided by commercial affiliations. CONCLUSIONS: There are considerable variations in the quality of WBHI on OLP in Arabic. Most WBHI resources were deemed to be of moderate quality at best. Providers of WBHI could benefit from increasing collaboration between commercial and academic institutions in creating WBHI and integrating guidance from international quality assessment tools to improve the quality and, hopefully, the utility of these valuable WBHI resources.