Detection Of Adverse Events Research Articles

Immune-related adverse event detection in liver cancer patients treated with immune checkpoint inhibitors: Nationwide exploratory survey in Japan.

This study aimed to comprehensively assess the incidence of immune-related adverse events (irAEs) and the detection systems in place for patients with liver cancer undergoing treatment with immune checkpoint inhibitors (ICIs), using a self-administered anonymous questionnaire. The questionnaire was designed to gather crucial insights into the management of irAEs in these patients. A self-administered anonymous questionnaire was sent to 456 liver disease collaborative base hospitals and cancer care coordination base hospitals in Japan. Responses were received from 112 facilities, indicating a response rate of 25%. The region with the highest response rate was Kanto (22%, 24 sites), followed by Kyushu (19%, 21 sites), Chubu (14%, 15 sites), and Kinki (14%, 15 sites). The number of patients with hepatocellular carcinoma (HCC) who received ICI treatment varied, with a mean±SD of 20.4±19.4 cases per year per facility. The number of full-time physicians who provided ICI treatment for HCC was 4.2±3.3 (mean±SD), ranging from 0 to 24 per facility. Of these, the majority included hepatologists and oncologists, whose numbers were 3.3±2.4 (mean±SD) (range, 0-11) and 0.8±1.0 (0-3), respectively. Gastroenterologists and internal medicine specialists participated in the treatment at some facilities. The survey results revealed that physicians administered ICI therapy for an average of 20 HCC cases per institution, with more than 17 types of irAEs reported. The most common irAEs were hepatic dysfunction, followed by thyroid dysfunction, skin disorders, interstitial pneumonia, and renal dysfunction.

Topological Reinforcement Adaptive Algorithm (TOREADA) Application to the Alerting of Convulsive Seizures and Validation with Monte Carlo Numerical Simulations

The detection of adverse events—for example, convulsive epileptic seizures—can be critical for patients suffering from a variety of pathological syndromes. Algorithms using remote sensing modalities, such as a video camera input, can be effective for real-time alerting, but the broad variability of environments and numerous nonstationary factors may limit their precision. In this work, we address the issue of adaptive reinforcement that can provide flexible applications in alerting devices. The general concept of our approach is the topological reinforced adaptive algorithm (TOREADA). Three essential steps—embedding, assessment, and envelope—act iteratively during the operation of the system, thus providing continuous, on-the-fly, reinforced learning. We apply this concept in the case of detecting convulsive epileptic seizures, where three parameters define the decision manifold. Monte Carlo-type simulations validate the effectiveness and robustness of the approach. We show that the adaptive procedure finds the correct detection parameters, providing optimal accuracy from a large variety of initial states. With respect to the separation quality between simulated seizure and normal epochs, the detection reinforcement algorithm is robust within the broad margins of signal-generation scenarios. We conclude that our technique is applicable to a large variety of event detection systems.

Using multiple drug similarity networks to promote adverse drug event detection

The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring.

Liver safety of tolvaptan in patients with autosomal dominant polycystic kidney disease: interim data from a post-authorization safety study.

After the risk of drug-induced liver injury was detected during tolvaptan clinical development for the treatment of autosomal dominant polycystic kidney disease (ADPKD), a post-marketing pharmacovigilance study was required for European Union regulatory approval. This is an interim analysis from a prospective, observational study enrolling patients prescribed tolvaptan for ADPKD in routine clinical practice. Data were obtained through physician records collected during regular care. Per the prescribing label, liver transaminases were to be monitored monthly for the first 18months of treatment and once every 3months thereafter. Patients and physicians were required to report adverse events suggestive of serious and potentially fatal liver injury. Data collection was from October 2016 to April 2022. Of 2074 patients (median follow-up 528days), alanine aminotransferase (ALT) or aspartate aminotransferase (AST) levels ≥3 times the upper limit of normal (ULN) were reported in 75 (3.6%) patients. At data cut-off, the enzyme elevations were confirmed for 65 patients. Among the 65 confirmed patients, in addition to transaminase elevations, there were 69 adverse events suggestive of liver injury. Tolvaptan was interrupted or withdrawn in 59/65 (90.8%) participants with confirmed ALT or AST ≥3times the ULN, with most transaminase elevations and adverse events resolved or resolving at data cut-off. No liver enzyme elevations met laboratory criteria for Hy's law cases. Transaminase elevations occurred post-marketing in a similar proportion of patients as reported in clinical trials (4.4-5.6%). Regular monitoring per label facilitates prompt detection of liver adverse events and intervention to mitigate the risk of severe injury.

Operationalizing an adverse event detection surveillance system to support antimicrobial stewardship activities: perceptions and insights from the SHEA research network.

A surveillance system for measuring patient-level antimicrobial adverse drug events (ADE) may support stewardship activities, however, design and implementation questions remain. In this national survey, stewardship experts favored simple, laboratory-based ADE definitions although there were tensions between feasibility, ability to identify attribution without chart review, and importance of specific ADE.

Time to detection of serious adverse events by continuous vital sign monitoring versus clinical practice.

Continuous vital sign monitoring detects far more severe vital sign deviations (SVDs) than intermittent clinical rounds, and deviations are to some extent related to subsequent serious adverse events (SAEs). Early detection of SAEs is pivotal to allow for effective interventions but the time relationship between detection of SAEs by continuous vital sign monitoring versus clinical practice is not well-described at the general ward. To quantify the time difference between detection of SAEs by continuous vital sign monitoring and clinical suspicion of deterioration (CSD) in major abdominal surgery patients. Five hundred and five patients had their vital signs continuously monitored in combination with usual clinical practice consisting of National Early Warning Score assessments at least every 8'th hour, assessments during rounds, and other kinds of staff-patient interactions. The primary outcome was the time difference between the first chart note of CSD versus the first SVD, detected by continuous vital sign monitoring, in patients with a subsequent confirmed SAE during or up to 48 h after end of continuous vital sign monitoring. Out of the 505 continuously monitored patients, 142 patients had a combination of both postoperative SAE, CSD and SVD, and thus were included in the primary analysis. The median time from the first SVD to SAE was 42.8 h (interquartile range 19.8-72.1 h) compared to 13 minutes (interquartile range - 4.8 to 3.5 h) for CSD with a median difference of 48.1 h (95% confidence interval 43.0-54.8 h), p-value < .001. Continuous vital sign monitoring detects signs of oncoming SAEs in the form of SVD hours before CSD, potentially allowing for earlier and more effective treatments to reduce the extent of SAEs.

Real-World Data Mining for Signal Detection of Antipsychotics-Associated Adverse Events Using the Korea Adverse Event Reporting System (KAERS) Database.

Background and Objectives: Recent studies suggest that the binary categorization of first-generation antipsychotics (FGAs) as being primarily responsible for extrapyramidal symptoms (EPSs) and second-generation antipsychotics (SGAs) for cardiometabolic abnormalities is an oversimplification. SGAs also demonstrate antagonistic affinity for D2 receptors, indicating their potential to induce EPSs. This study utilized the Korea Adverse Event Reporting System (KAERS) database to explore adverse drug event (ADE) signals related to both FGAs and SGAs. Materials and Methods: Relevant ADE reports from January 2013 to December 2022 were extracted from the KAERS database and analyzed using disproportionality analysis, employing the proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) with its 95% lower confidence interval (LCI) indices. Results: Of the initial dataset of 2,890,702 ADE reports, those with insufficient data and duplicates were removed, resulting in a final dataset of 5249 reports for analysis. Aripiprazole, an SGA, showed signals for movement disorders, including EPSs (PRR 4.7, ROR 4.8, IC 2.2), tremors (PRR 5.3, ROR 5.4, IC 2.4), and akathisia (PRR 18.6, ROR 19.3, IC 3.5). Notably, for quetiapine, cardiovascular signals were detected, including increased blood pressure (PRR 2.1, ROR 2.3, IC 0.5), and tachyarrhythmia (PRR 13.9, ROR 14.1, IC 1.8), along with peripheral edema (PRR 2.5, ROR 2.5, IC 0.2). Metabolic abnormalities, such as weight gain and increased appetite, were identified for four SGAs: aripiprazole, olanzapine, quetiapine, and risperidone. Safety signals related to movement disorders were not detectable for FGAs, likely due to the limited number of ADE reports available for analysis. Conclusions: Our study findings support that the distribution of ADEs between FGAs and SGAs is not strictly binary. Aripiprazole, despite being an SGA, showed signals for extrapyramidal movement disorders. Four SGAs (aripiprazole, olanzapine, quetiapine, and risperidone) were linked to metabolic side effects, while quetiapine was associated with cardiovascular safety signals.

Cardiac toxicity of HER-2 targeting antibody-drug conjugates: overview and clinical implications.

Antibody-drug conjugates (ADCs) have recently emerged as a promising therapeutic option that combine the specificity of monoclonal antibodies and the cytotoxic effect of chemotherapy. With numerous ADCs approved and on the market, a particular concern of ADCs that target HER-2 has been their cardiac side effects, in view of the crucial role of HER-2 in cardiac development and physiology. While rarely toxic and generally safe, numerous publications have outlined the consistent association of trastuzumab emtansine (T-DM1) and trastuzumab deruxtecan (T-DXd) with the development of cardiac toxicity. Despite not being clinically relevant in most cases, cardiac baseline evaluation, monitoring and early detection of cardiac adverse events remain pivotal with HER-2 targeting ADCs. This review aims to summarize and better characterize the complete cardiac toxicity profile of HER-2 ADCs, with the goal of improving clinical understanding of this adverse event, leading to better recognition, monitoring and management.

Real-Time Monitoring of Seawater Quality Parameters in Ayia Napa, Cyprus

Real-time monitoring systems are crucial for the comprehensive management of operations and processes, as well as for assessing the impacts of coastal infrastructures on the marine environment. These systems not only support environmental protection and data-driven decision-making but also enable the early detection of adverse events and the issuance of timely warnings for prompt responses. Although water quality is a critical parameter in this monitoring framework, there are currently limited permanent systems in place dedicated to maintaining these objectives. Even fewer systems leverage their data for research purposes, leading to a gap in the literature regarding effective processing approaches for real-time water quality data. In this context, this study presents a real-time water quality monitoring system integrated into a broader in-field laboratory installed at a coastal area off the coast of Ayia Napa, Cyprus, as well as an initial measured data set of different qualitative quantities. It proposes a holistic approach for post-processing real-time seawater quality data, employing both time and frequency domain analyses, alongside filtering techniques. The study discusses the advantages of each method and emphasizes the importance of their combined use. Utilizing data collected from a three-month operational period, the study assesses the current state of marine seawater quality and examines both temporal and cyclic variations in various seawater quality parameters. The findings reveal that the examined seawater parameters are within reasonable values, indicating that the construction and operation of a nearby marina and the necessary infrastructures (e.g., breakwater) did not affect the seawater quality in the area. Additionally, the study identifies pronounced daily cyclic responses in different seawater quality parameters, including temperature, density, pH, dissolved oxygen, and turbidity. Finally, notable correlations are observed between temperature and dissolved oxygen, temperature and conductivity, oxidation–reduction potential (ORP) and salinity, ORP and dissolved oxygen, and ORP and TDS.

Estimation of the maximum potential cost saving from reducing serious adverse events in hospitalized patients.

The increasing use of advanced medical technologies to detect adverse events, for instance, artificial intelligence-assisted technologies, has shown promise in improving various aspects within health care but may also come with substantial expenses. Therefore, understanding the potential economic benefits can guide decision-making processes regarding implementation. We aimed to estimate the potential cost savings associated with reducing length of stay and avoiding readmissions within the framework of an artificial intelligence-assisted vital signs monitoring system. We used data from Danish national registries and coarsened exact matching to estimate the difference in length of stay and probability of readmission among adult in-hospital patients exposed to and not exposed to serious adverse events. We used these estimates to calculate the maximum potential savings that could be achieved by early detection of adverse events to reduce length of stay and avoid readmissions. Patients exposed to serious adverse events during admission had 2.4 (95% CI: 2.4-2.5) additional hospital bed days and had 14% (95% CI 11%-17%) higher odds of readmissions compared with patients not exposed to such events. A base case scenario yielded maximum potential savings if one patient avoided a serious adverse event of EUR 2040 due to reduced length of stay and EUR 43 due to avoidance of readmissions caused by serious adverse events. Reductions in serious adverse events are associated with decreased healthcare costs due to reduced length of stay and avoided readmissions. Artificial intelligence-assisted vital signs monitoring systems are one potential approach to reduce serious adverse events, however, the ability of this technology to reduce adverse events remains unclear. Comprehensive prospective analyses of such systems including the intervention and implementation costs are necessary to understand their full economic impact.

Smoking and sudden cardiac death in patients with previous coronary artery disease.

Smoking is a known risk for sudden cardiac death (SCD) in the general population. However, its significance in patients with acute coronary syndrome (ACS), a condition that also elevates the risk of SCD, is disputable. A total of 9704 consecutive ACS patients with available smoking data were included in the analysis. Comprehensive patient data were obtained from the Mass Data in Detection and Prevention of Serious Adverse Events in Cardiovascular Disease research database. A composite endpoint of SCD, SCD aborted by successful resuscitation and accurate implantable cardioverter defibrillator therapy to otherwise potentially fatal ventricular fibrillation/ventricular tachycardia was used. Univariate, age- and sex-adjusted, and a multivariate fine-gray competing risk regression with adjustment to traditional risk factors was conducted. Median follow-up time was 6.8 years (IQR, 4.1-10.2), and 454 (4.7%) SCD cases were identified. At the baseline, 23.7% (N = 2444) were active smokers, and 20.8% (N = 2146) were ex-smokers. In the multivariate model, active smokers had an elevated risk of 1.79 (95% CI, 1.41-2.27; P < 0.001) for future SCD. Ex-smokers had no elevated risk for SCD in fine-gray subdistribution hazard. Also, active smokers were notably younger (mean age 58.7 years) than non- or ex-smokers (71.1 years and 68.9 years, respectively, P < 0.001 for both comparisons). Active smokers had a 79% higher risk of SCD when compared with nonsmokers. Smoking cessation should be heavily encouraged after ACS. Also, a person's smoking status should be considered in further studies developing SCD and implantable cardioverter defibrillator-benefit risk scores.

Prevention, detection and management of adverse events of third-space endoscopy.

Third space endoscopy (TSE) or sub-mucosal endoscopy using a mucosal flap valve (SEMF) enables the endoscopist to operate in the deeper layers of the gastrointestinal tract or gain access to the mediastinal/peritoneal cavity for natural orifice transoral endoscopic surgery (NOTES). TSE procedures are essentially endoscopic surgical procedures with a variable learning curve. Adverse events (AEs) during TSE are specific and follow a certain pattern across the spectrum of TSE procedures. These can be broadly categorized according to either type of AE, time of presentation relative to the procedure or according to degree of severity. Three major categories of AEs encountered during TSE include insufflation related AEs, mucosal injuries (MIs) and bleeding. Other relevant AEs include infectious complications, aspiration pneumonia, post-procedural chest/abdominal pain, atelectasis, cardiac arrhythmias, pleural effusion and pulmonary embolism. Reported incidence of AEs during TSE procedures varies according to the type and complexity of procedure. Acquaintance regarding potential risk factors, technical tips and precautions, alarm signs for early recognition, assessment of degree of severity, morphological characterization of AEs and finally, expeditious selection of appropriate management strategy are crucial and imperative for successful clinical outcomes. The current review discusses the current evidence and practical guidelines for prevention, early detection and management of TSE-related AEs.

Improving Detection of Hospital Adverse Events Using Machine Learning on Real-World Narrative EMR Data

ObjectiveAdministrative data often underrepresents hospital adverse events (AEs) due to limitations in International Classification of Diseases (ICD) coding. By leveraging electronic medical records (EMRs), we aim to mitigate these discrepancies and enhance the precision of healthcare surveillance and performance evaluations. To this end, we have developed a machine learning (ML)-based approach that utilizes EMR text data to detect common AEs. MethodsWe sampled adult admissions from four Calgary hospitals (2017 - 2022). Registered nurses assessed charts for 17 AEs, and the results were used as reference standard. We compared two AE detection methods: the standard ICD-based method following Canadian guidelines, and our ML algorithm applied to EMR narratives. Sensitivity, positive predictive value (PPV), negative predictive value (NPV), and specificity for both methods were calculated and compared against the reference standard. ResultsWe analyzed 9,566 patients, of whom 1,506 were identified with AEs. Of the 17 AEs, the sensitivity in ICD-coded data ranged from 0-37%, and in EMRs, it was between 75-100%. Both showed low PPV (0-50% ICD vs.1-34% EMR). ICD data had high specificity ranging from 99-100% and NPV (99%-100%), while EMRs had specificity between 68-94% and an NPV of 100%. Conclusion and ImplicationsML significantly enhances sensitivity for AE detection compared to ICD-10-CA coding, despite both methods experiencing low PPV due to imbalances in EMR data. This marked improvement in sensitivity highlights ML's potential to transform AE surveillance and reporting, promising significant advancements in patient safety and healthcare quality by enabling more accurate and comprehensive identification of AEs.

The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.

Adverse drug events pose an enormous public health burden, leading to hospitalization, disability, and death. Even the adverse events (AEs) categorized as nonserious can severely impact on patient's quality of life, adherence, and persistence. Monitoring medication safety is challenging. Web-based patient reports on social media may be a useful supplementary source of real-world data. Despite the growth of sophisticated techniques for identifying AEs using social media data, a consensus has not been reached as to the value of social media in relation to more traditional data sources. This study aims to evaluate and characterize the utility of social media analysis in adverse drug event detection and pharmacovigilance as compared with other data sources (such as spontaneous reporting systems and the clinical literature). In this scoping review, we searched 11 bibliographical databases and Google Scholar, followed by handsearching and forward and backward citation searching. Each record was screened by 2 independent reviewers at both the title and abstract stage and the full-text screening stage. Studies were included if they used any type of social media (such as Twitter or patient forums) to detect AEs associated with any drug medication and compared the results ascertained from social media to any other data source. Study information was collated using a piloted data extraction sheet. Data were extracted on the AEs and drugs searched for and included; the methods used (such as machine learning); social media data source; volume of data analyzed; limitations of the methodology; availability of data and code; comparison data source and comparison methods; results, including the volume of AEs, and how the AEs found compared with other data sources in their seriousness, frequencies, and expectedness or novelty (new vs known knowledge); and conclusions. Of the 6538 unique records screened, 73 publications representing 60 studies with a wide variety of extraction methods met our inclusion criteria. The most common social media platforms used were Twitter and online health forums. The most common comparator data source was spontaneous reporting systems, although other comparisons were also made, such as with scientific literature and product labels. Although similar patterns of AE reporting tended to be identified, the frequencies were lower in social media. Social media data were found to be useful in identifying new or unexpected AEs and in identifying AEs in a timelier manner. There is a large body of research comparing AEs from social media to other sources. Most studies advocate the use of social media as an adjunct to traditional data sources. Some studies also indicate the value of social media in understanding patient perspectives such as the impact of AEs, which could be better explored. RR2-10.2196/47068.

Role of quantitative imaging biomarkers in an early FDG-PET/CT for detection of immune-related adverse events in melanoma patients: a prospective study.

To evaluate the role of the novel quantitative imaging biomarker (QIB) SUVX% of 18F-FDG uptake extracted from early 18F-FDG-PET/CT scan at 4 weeks for the detection of immune-related adverse events (rAE) in a cohort of patients with metastatic melanoma (mM) patients receiving immune-checkpoint inhibitors (ICI). In this prospective non-interventional, one-centre clinical study, patients with mM, receiving ICI treatment, were regularly followed by 18F-FDG PET/CT. Patients were scanned at baseline, early point at week four (W4), week sixteen (W16) and week thirty-two (W32) after ICI initiation. A convolutional neural network (CNN) was used to segment three organs: lung, bowel, thyroid. QIB of irAE - SUVX% - was analyzed within the target organs and correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. A total of 242 18F-FDG PET/CT images of 71 mM patients were prospectively collected and analysed. The early W4 scan showed improved detection only for the thyroid gland compared to W32 scan (p=0.047). The AUROC for detection of irAE in the three target organs was highest when SUVX% was extracted from W16 scan and was 0.76 for lung, 0.53 for bowel and 0.81 for thyroid. SUVX% extracted from W4 scan did not improve detection of irAE compared to W16 scan (lung: p = 0.54, bowel: p = 0.75, thyroid: p = 0.3, DeLong test), as well as compared to W32 scan in lungs (p = 0.32) and bowel (p = 0.3). Early time point 18F-FDG PET/CT at W4 did not lead to statistically significant earlier detection of irAE. However, organ 18F-FDG uptake as quantified by SUVX% proved to be a consistent QIB of irAE. To better assess the role of 18F-FDG PET/CT in irAE detection, the time evolution of 18F-FDG PET/CT quantifiable inflammation would be of essence, only achievable in multi centric studies.

A process mining model to detect adverse patient safety events: Blood products watch

Introduction Patient safety incidents and adverse events are reported voluntarily by healthcare staff. The Global Trigger Tool, developed by the Institute for Healthcare Improvement, is used to capture unreported events. The objective of the new model is to use process mining in the context of adverse event detection to increase reporting efficiency. Methods In the first phase, the process mining model is used to discover the actual process map by querying inpatient processes using the provided log data. Transfusions of blood or blood products within 24 h after surgery, a subset of The Transfusion of Blood or Use of Blood Products trigger of Institute for Healthcare Improvement (GTT-C1) was deployed as a process pattern within the actual process map. Only detected patients were reviewed in the last phase. Results The model ran on a single hospital data set, which included 2870 patients, 1048 of which included surgeries. The model reduced the analyzed data to 57 patients. The expert group analyzed these 57 patients and defined 12 adverse events. The calculated adverse event ratio was 1.1%, comparable with other manual Global Trigger Tool studies. In addition, the reviewers’ time required decreased by 94.6%, from 138.28 to 7.52 h. Two reviewers validated the model by reviewing a sample of patients. Conclusions Process mining can be used in healthcare institutions to conduct routine surveillance and detect unreported safety events. This allows healthcare providers to allocate financial and human resources to more urgent needs and increase efficiency. Further research is needed to evaluate the model with other triggers.

Protocol for the development and validation of a trigger tool for antiretroviral ADE detection among adult HIV/AIDS patients: A short communication

Antiretroviral therapy (ART) is a lifelong combination therapy which has been very promising in managing HIV/AIDs but reported to have several adverse events on the recipients, affecting the desired antiretroviral therapeutic goals. Adverse drug event detection and prevention therefore become very important safety culture maintenance tools among patients living with HIV/AIDS. The designed protocol provided step by step approaches to develop and validate a trigger tool for detection of adverse drug events (ADE) among adult HIV/AIDS patients. The approaches involved include a baseline (Pre-trigger tool development) retrospective clinical record review to establish a baseline ADE detection capacity of a conventional non-trigger tool method; the Delphi panel process to develop the trigger tool in question; Face and content validity approach for validation of the developed trigger tool; Pilot test of the trigger tool using small clinical record sample; determining the effectiveness of the developed trigger tool through the retrospective clinical record review of same samples reviewed at the baseline. Finally, the protocol described the process of determining the feasibility of the developed trigger tool through the survey of the clinical record review team members.

Maternal Health Disparities in Acute Care Rehabilitation.

Patients with a high-risk pregnancy or who immediately give birth are often not provided access to acute care occupational and physical therapy during their hospital admission. The greatest potential for health care crises, including maternal mortality, occurs during the first 6 weeks after birth. Maternal mortality and morbidity, which continue to rise in the United States, are considered indicators of the overall population health of a country. It is estimated that over 80% of pregnancy-related deaths are preventable. The leading causes of death within the first 6 days after birth are hemorrhage, hypertensive disorders, and infection. Implicit biases about childbearing individuals who are perceived to be relatively young, independent, active, and healthy may unintentionally contribute to areas of substandard care that adversely impact maternal health outcomes. It is a professional and ethical imperative that health providers, including occupational and physical therapists, establish themselves as allies to patient groups at high risk for disparate treatment. The Acute Care OB Rehab Implicit Bias Self-Reflection Tool is an instrument designed for health providers to identify stereotypes that may contribute to inequitable access to rehabilitation in the hospital after birth. Including rehabilitation services for the perinatal population in the hospital setting can play a pivotal role in early detection of adverse events with activity that may directly impact the increasing maternal mortality rate in the United States.

Utility of serial troponin testing for emergency department patients with syncope.

For emergency department (ED) patients with syncope, cardiac troponin can identify acute coronary syndrome (ACS) and prognosticate for 30-day serious adverse events. However, it is unclear if serial testing improves diagnostic yield and prognostication. This was a secondary analysis of data from two prospective studies conducted to develop the Canadian Syncope Risk Score. Adults (age ≥ 16 years) with syncope were enrolled, and patient characteristics, vital signs, physician diagnostic impression, electrocardiogram and troponin results, and adjudicated 30-day serious adverse event were collected. The primary outcome was the detection of a serious adverse event within 30 days of ED disposition. The secondary outcome was comparison of ED length of stay among patients with single versus serial troponin measurements. 4996 patients [mean age 64.5 (SD 18.8) years, 52.2% male] were included: 4397 (89.8%) with single troponin [232 (5.3%) with serious adverse event in the ED and 203 (4.6%) after ED disposition]; 499 (10.2%) patients with > 1 troponin measurement [39 (7.8%) with serious adverse event in ED and 60 (12.0%) after ED disposition]. Among those with serial measurements, 10 patients (2.0%) had a rise from below to above the 99th percentile threshold, of whom 4 patients (0.8%) suffered serious adverse event: two with arrhythmias diagnosed on electrocardiogram, one with ACS and one suffered respiratory failure. Nine patients (1.8%) had Canadian Syncope Risk Score risk reclassification based on serial measurement, and none suffered 30-day serious adverse event. Median ED length of stay was significantly longer for patients with serial testing (5.6 vs. 3.8 h, p < 0.001). The initial troponin measurement was sufficient for serious adverse event detection and in-ED risk stratification. Serial troponin testing does not improve the diagnostic yield or prognostication and should be reserved for patients with ongoing symptoms or electrocardiogram findings suggestive of cardiac ischemia.

Effect of digital tools to promote hospital quality and safety on adverse events after discharge.

Post-discharge adverse events (AEs) are common and heralded by new and worsening symptoms (NWS). We evaluated the effect of electronic health record (EHR)-integrated digital tools designed to promote quality and safety in hospitalized patients on NWS and AEs after discharge. Adult general medicine patients at a community hospital were enrolled. We implemented a dashboard which clinicians used to assess safety risks during interdisciplinary rounds. Post-implementation patients were randomized to complete a discharge checklist whose responses were incorporated into the dashboard. Outcomes were assessed using EHR review and 30-day call data adjudicated by 2 clinicians and analyzed using Poisson regression. We conducted comparisons of each exposure on post-discharge outcomes and used selected variables and NWS as independent predictors to model post-discharge AEs using multivariable logistic regression. A total of 260 patients (122 pre, 71 post [dashboard], 67 post [dashboard plus discharge checklist]) enrolled. The adjusted incidence rate ratios (aIRR) for NWS and AEs were unchanged in the post- compared to pre-implementation period. For patient-reported NWS, aIRR was non-significantly higher for dashboard plus discharge checklist compared to dashboard participants (1.23 [0.97,1.56], P = .08). For post-implementation patients with an AE, aIRR for duration of injury (>1 week) was significantly lower for dashboard plus discharge checklist compared to dashboard participants (0 [0,0.53], P < .01). In multivariable models, certain patient-reported NWS were associated with AEs (3.76 [1.89,7.82], P < .01). While significant reductions in post-discharge AEs were not observed, checklist participants experiencing a post-discharge AE were more likely to report NWS and had a shorter duration of injury. Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge. NCT05232656.