Citations

Objectives: Electronic health records (EHR)-based triggers (eTriggers) have been used to study diagnostic errors in the emergency department (ED), often with suboptimal performance. Our objective was to investigate incremental value of multi-factor machine learning (ML) approaches to improve eTrigger performance. Methods: Patients presenting to an academic ED were categorized into trigger-positive and trigger-negative using standard trigger (T) definitions: (T1) ED return visits resulting in admission within 10 days; (T2) care escalation from the inpatient unit to the ICU within 24 hours; and (T3) deaths within 24 hours of admission. We trained and evaluated 6 supervised ML models. Results: A total of 124,053 consecutive encounters (5791 T-positive and 118,262 T-negative) were included. Among the T-positive, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. The T-based positive predictive values (PPV) were 5.2% for T1, 8.2% for T2, and 6.5% for T3. ML models trained and evaluated on balanced training dataset and imbalanced test set had low classification performances (accuracy: 0.72–0.95; PPV: 0.00–0.16; F1-score: 0.00–0.23). Higher performances were observed in balanced test sets (accuracy: 0.80–0.97; PPV: 0.82–1.00; F1-score: 0.79–0.97). Comparing models trained on clinically annotated data with models trained on T-based labels identified other important factors. Conclusions: Utilizing machine learning to refine e-triggers slightly improves the identification of diagnostic errors, as evidenced by an increase in PPV values. We identified new potential factors contributing to ED diagnostic errors. These findings open new avenues to construct or modify more accurate e-triggers for diagnostic error identification.

Importance Diagnostic errors represent a major patient safety concern, with the potential to significantly impact patient outcomes. To address this, various trigger-based strategies have been developed to identify diagnostic errors, aiming to enhance clinical decision-making and improve patient safety. Objective To evaluate the performance of three pre-established triggers (T) in the emergency department (ED) setting and assess their effectiveness in detecting diagnostic errors. Design Consecutive cohort, retrospective observational design. Setting Academic ED with 80 000 annual visits. Participants Adults and children presenting to a single ED in the USA between 1 May 2018 and 1 January 2020. Intervention/outcomes Electronic health records (EHRs) were retrieved and categorised into trigger-positive and trigger-negative cases using the following criteria: T1—unscheduled returnvisits to the ED with admission within 7–10 days of theinitial visit; T2—care escalation from the inpatient unitto the intensive care unit (ICU) within 6, 12 or 24 hoursof ED admission; and T3—all deaths in the ED or within24 hours of ED admission, excluding palliative care. A random sample of trigger-positive cases was reviewed using the SaferDx tool to determine the presence or absence of a diagnostic error. Results A total of 5791 trigger-positive and 118262 trigger-negative cases were identified. Among trigger-positive cases, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. A preliminary chart review of 462 trigger-positive and 251 trigger-negative cases showed most were error-negative (279 and 217, respectively). Detailed reviews found 32 diagnostic errors among 183 trigger-positive cases, yielding PPVs of 5.4% (T1), 8.9% (T2), and 6.9% (T3). No errors were found in 34 reviewed trigger-negative cases, resulting in a 100% NPV. Sepsis was the most common diagnosis among error-positive cases (n=11, 34.4%). Those with non-specific chief complaints like altered mental status or shortness of breath had higher diagnostic error risk. Conclusion and relevance While previously proposed EHR-based triggers can identify some diagnostic errors, they are insufficient for detecting all cases. To improve error detection performance, we recommend exploring data-driven strategies, such as machine learning techniques, to more effectively identify underlying contributing factors to diagnostic errors and enhance detection accuracy in the ED.

Neurogastroenterology and motility disorders are complex gastrointestinal conditions that are prevalent worldwide, particularly affecting women and younger individuals. These conditions significantly impact the quality of life of people suffering from them. There is increasing evidence linking these disorders to cirrhosis, with a higher prevalence compared to the general population. However, the link between neurogastroenterology and motility disorders and cirrhosis remains unclear due to undefined mechanisms. In addition, managing these conditions in cirrhosis is often limited by the adverse effects of drugs commonly used for these disorders, presenting a significant clinical challenge in the routine management of patients with cirrhosis. This review delves into this connection, exploring potential pathophysiological links and clinical interventions between neurogastroenterology disorders and cirrhosis.

LINKED CONTENTThis article is linked to Idalsoaga et al papers. To view these articles, visithttps://doi.org/10.1111/apt.18193andhttps://doi.org/10.1111/apt.18220

Summary Background Oesophageal disorders and chronic liver disease are common worldwide and significantly impact quality of life. The intricate link between these conditions, including how oesophageal disorders like GERD, Barrett's oesophagus and oesophageal cancer affect and are affected by chronic liver disease, remains poorly understood. Aims To review the relationship between oesophageal disorders and chronic liver disease, evaluating epidemiology, pathophysiology and therapeutic factors. Methods We reviewed the literature on the relationship between oesophageal disorders and chronic liver disease, including cirrhosis, using the PubMed database Results Oesophageal disorders such as gastroesophageal reflux disease, Barrett's oesophagus, oesophageal cancer, oesophageal motor disorders and oesophageal candidiasis are prevalent among individuals with cirrhosis, exacerbating the burden of liver disease. These diseases have a multifaceted symptomatology and pathogenic basis, posing a significant challenge in cirrhotic patients that necessitates careful diagnosis and management. Additionally, therapies frequently used for these diseases, such as proton pump inhibitors, require careful consideration in cirrhotic patients due to potential adverse effects and altered pharmacokinetics. Managing oesophageal disorders in cirrhotic patients requires a cautious approach due to possible interactions with medications and the risk of adverse effects. Furthermore, symptoms associated with these conditions are often exacerbated by common interventions in patients with cirrhosis, such as band ligation for oesophageal varices. Conclusions Oesophageal disorders are common in cirrhosis and increase the disease burden. These conditions require careful management due to complex symptoms and treatment risks. Proton pump inhibitors and other therapies must be used cautiously, as cirrhosis interventions can worsen symptoms.

Abstract Purpose Rapid sequence intubation (RSI) is a common emergency department (ED) procedure with an associated complication of postintubation hypotension (PIH). It has not been clearly established whether the selection and dose of induction agent affect risk of PIH. The objective of this study was to determine the incidence of PIH in patients receiving full-dose compared to reduced-dose induction agent for RSI in the ED. Methods This was a health system–wide, retrospective cohort study comparing incidence of PIH based on the induction medication and dose given for RSI in the ED. Patients were included if they underwent RSI from July 1, 2018, through December 31, 2020, were 18 years of age or older, and received etomidate or ketamine. A reduced dose was defined as a ketamine dose of 1.25 mg/kg or less and an etomidate dose of 0.2 mg/kg or less. Results A total of 909 patients were included in the final analysis, with most receiving etomidate (n = 764; 84%) and a smaller number receiving ketamine (n = 145; 16%). Patients who received ketamine had a higher mean pre-intubation shock index (full dose, 1.08; reduced dose, 1.04) than those who received etomidate (full dose, 0.89; reduced dose, 0.92) (P ≤ 0.001). Reduced doses of induction agent were observed for 107 patients receiving etomidate (14.0%) and 60 patients receiving ketamine (41.4%). Patients who received full-dose ketamine for induction had the highest rate of PIH (n = 31; 36.5%), and the difference was statistically significant compared to patients receiving reduced-dose ketamine (16.7%; P = 0.021) and full-dose etomidate (22.8%; P = 0.010). Conclusion We observed that full-dose ketamine was associated with the highest rate of PIH; however, this group had the poorest baseline hemodynamics, confounding interpretation. Our results do not support broad use of a reduced-dose induction agent.