Características clínicas de reanimações cardiopulmonares intra-hospitalares registradas em prontuário: estudo transversal / Clinical characteristics of in-hospital cardiopulmonary resuscitations recorded in medical records: a cross-sectional study / Características clínicas de las reanimaciones cardiopulmonares intrahospitalarias registradas en las historias clínicas: estudio transversal

Objetivo: identificar características clínicas das paradas cardiopulmonares e reanimações cardiopulmonares ocorridas em ambiente intra-hospitalar. Método: estudo quantitativo, prospectivo e observacional, a partir de informações de prontuários de pacientes submetidos a manobras de reanimação devido à parada cardiopulmonar entre janeiro e dezembro de 2021. Utilizou-se um instrumento baseado nas variáveis do modelo de registro Utstein. Resultados: em 12 meses foram registradas 37 paradas cardiopulmonares. A maioria ocorreu na unidade de terapia intensiva respiratória, com causa clínica mais prevalente hipóxia. 65% dos pacientes foram intubados no atendimento e 57% apresentaram ritmo atividade elétrica sem pulso. A duração da reanimação variou entre menos de cinco a mais de 20 minutos. Como desfecho imediato, 57% sobreviveram. Conclusão: dentre os registros analisados, a maior ocorrência de paradas cardiopulmonares foi na unidade de terapia intensiva respiratória, relacionada à Covid-19. Foram encontrados registros incompletos e ausência de padronização nas condutas.

Objective: identify the clinical characteristics of cardiopulmonary arrests and cardiopulmonary resuscitations in the in-hospital environment. Method: this is a quantitative, prospective and observational study based on information from the medical records of patients who underwent resuscitation maneuvers due to cardiopulmonary arrest between January and December 2021. An instrument based on the variables of the Utstein registration protocol was used. Results: thirty-seven cardiopulmonary arrests were recorded in 12 months. The majority occurred in a respiratory intensive care unit, with hypoxia being the most prevalent clinical cause. Sixty-five percent of the patients were intubated and 57% had pulseless electrical activity. The duration of resuscitation ranged from less than five to more than 20 min. As for the immediate outcome, 57% survived. Conclusion: among the records analyzed, the highest occurrence of cardiopulmonary arrests was in respiratory intensive care units, and they were related to Covid-19. Moreover, incomplete records and a lack of standardization in cardiopulmonary resuscitation procedures were found.

Objetivo: Identificar las características clínicas de paros cardiopulmonares y reanimaciones cardiopulmonares que ocurren en un ambiente hospitalario. Método: estudio cuantitativo, prospectivo y observacional, realizado a partir de información presente en historias clínicas de pacientes sometidos a maniobras de reanimación por paro cardiorrespiratorio entre enero y diciembre de 2021. Se utilizó un instrumento basado en las variables del modelo de registro Utstein. Resultados: en 12 meses se registraron 37 paros cardiopulmonares. La mayoría ocurrió en la unidad de cuidados intensivos respiratorios, la causa clínica más prevalente fue la hipoxia. El 65% de los pacientes fue intubado durante la atención y el 57% presentaba un ritmo de actividad eléctrica sin pulso. La duración de la reanimación varió entre menos de cinco y más de 20 minutos. Como resultado inmediato, el 57% sobrevivió. Conclusión: entre los registros analizados, la mayor cantidad de paros cardiopulmonares se dio en la unidad de cuidados intensivos respiratorios, relacionada con Covid-19. Se encontraron registros incompletos y falta de estandarización en el procedimiento.

The incremental design of a machine learning framework for medical records processing.

OBJECTIVES: This work presents the development and evaluation of coordn8, a web-based application that streamlines fax processing in outpatient clinics using a "human-in-the-loop" machine learning framework. We demonstrate the effectiveness of the platform at reducing fax processing time and producing accurate machine learning inferences across the tasks of patient identification, document classification, spam classification, and duplicate document detection. METHODS: We deployed coordn8 in 11 outpatient clinics and conducted a time savings analysis by observing users and measuring fax processing event logs. We used statistical methods to evaluate the machine learning components across different datasets to show generalizability. We conducted a time series analysis to show variations in model performance as new clinics were onboarded and to demonstrate our approach to mitigating model drift. RESULTS: Our observation analysis showed a mean reduction in individual fax processing time by 147.5 s, while our event log analysis of over 7000 faxes reinforced this finding. Document classification produced an accuracy of 81.6%, patient identification produced an accuracy of 83.7%, spam classification produced an accuracy of 98.4%, and duplicate document detection produced a precision of 81.0%. Retraining document classification increased accuracy by 10.2%. DISCUSSION: coordn8 significantly decreased fax-processing time and produced accurate machine learning inferences. Our human-in-the-loop framework facilitated the collection of high-quality data necessary for model training. Expanding to new clinics correlated with performance decline, which was mitigated through model retraining. CONCLUSION: Our framework for automating clinical tasks with machine learning offers a template for health systems looking to implement similar technologies.

Content Analysis of Serious Illness Conversation Documentation: Structured vs. Free-Text Information.

CONTEXT: Clear, accessible, and thorough documentation of serious illness conversations helps ensure that critical information patients share with clinicians is reflected in their future care. OBJECTIVES: We sought to characterize and compare serious illness conversations recorded two different ways in the electronic health record to better understand patterns of serious illness conversation documentation. METHODS: We performed content analysis of serious illness conversations documented in the electronic health record, whether documented via structured tab or free-text clinical notes, for patients (n=150) with advanced cancer who started a treatment associated with a poor prognosis between October 2020 and June 2022. A multidisciplinary team iteratively developed a codebook to classify serious illness conversation content (e.g., goals/hopes) on a preliminary sample (n=30), and two researchers performed mixed deductive-inductive coding on the remaining data (n=120). We reviewed documentation from 34 patients with serious illness conversations documentation in the structured tab only, 43 with documentation in only free-text clinical notes, and 44 with documentation of both types. We then compared content between documentation types. RESULTS: Information documented more frequently in structured tabs included fears/worries and illness understanding; clinical notes more often included treatment preferences, deliberations surrounding advance directives, function, and trade-offs. Qualitative insights highlight a range of length and detail across documentation types, and suggest notable authorship by palliative and social work clinicians. CONCLUSION: How serious illness conversations are documented in the electronic health record may impact the content captured. Future quality improvement efforts should seek to consolidate documentation sources to improve care and information retention.

Diversity and Ecological Distribution of Plecoptera of Minas Gerais, Brazil.

The order Plecoptera constitutes a relatively small group of aquatic insects, encompassing 17 extant families and comprising over 4400 valid species. In Brazil, the number of valid extant species is 207, located in two families: Perlidae (149) and Gripopterygidae (58). Despite extensive research on the southeastern region of Brazil, there is a notable scarcity of comprehensive studies consolidating geographical records and species richness of Plecoptera in the state of Minas Gerais. This study seeks to increase and refine our understanding of Plecoptera within Minas Gerais, focusing on its diversity and distribution. The initial phase involved a thorough review of articles documenting Plecoptera species in the state. Subsequently, biological material from the Museum of Entomology at the Federal University of Viçosa collection was meticulously identified, and its geographical records were incorporated. Utilizing this dataset, we compiled an updated list of Plecoptera species documented in Minas Gerais. Geographical coordinates of collection points were then mapped and graphically represented to elucidate the geographic and altitudinal distribution of these species. A total of 42 Plecoptera species were identified within the state of Minas Gerais, adding many occurrence records and documenting the first record of Gripopteryx pinima for the state. Despite these advancements, knowledge gaps persist, particularly in the mesoregions of Triângulo/Alto Paranaíba, Oeste de Minas, Vale do Mucuri, and Campo das Vertentes. This endeavor serves as an initial foundation to stimulate further collections and investments in undersampled areas, fostering future monitoring and conservation initiatives for aquatic environments.

Retrospective analysis of hospital electronic health records reveals unseen cases of acute hepatitis with unknown aetiology in adults in Oxfordshire.

BACKGROUND: An outbreak of acute severe hepatitis of unknown aetiology (AS-Hep-UA) in children during 2022 was subsequently linked to infections with adenovirus-associated virus 2 and other 'helper viruses', including human adenovirus. It is possible that evidence of such an outbreak could be identified at a population level based on routine data captured by electronic health records (EHR). METHODS: We used anonymised EHR to collate retrospective data for all emergency presentations to Oxford University Hospitals NHS Foundation Trust in the UK, between 2016-2022, for all ages from 18 months and older. We investigated clinical characteristics and temporal distribution of presentations of acute hepatitis and of adenovirus infections based on laboratory data and clinical coding. We relaxed the stringent case definition adopted during the AS-Hep-UA to identify all cases of acute hepatitis with unknown aetiology (termed AHUA). We compared events within the outbreak period (defined as 1st Oct 2021-31 Aug 2022) to the rest of our study period. RESULTS: Over the study period, there were 903,433 acute presentations overall, of which 391 (0.04%) were classified as AHUA. AHUA episodes had significantly higher critical care admission rates (p < 0.0001, OR = 41.7, 95% CI:26.3-65.0) and longer inpatient admissions (p < 0.0001) compared with the rest of the patient population. During the outbreak period, significantly more adults (≥ 16 years) were diagnosed with AHUA (p < 0.0001, OR = 3.01, 95% CI: 2.20-4.12), and there were significantly more human adenovirus (HadV) infections in children (p < 0.001, OR = 1.78, 95% CI:1.27-2.47). There were also more HAdV tests performed during the outbreak (p < 0.0001, OR = 1.27, 95% CI:1.17-1.37). Among 3,707 individuals who were tested for HAdV, 179 (4.8%) were positive. However, there was no evidence of more acute hepatitis or increased severity of illness in HadV-positive compared to negative cases. CONCLUSIONS: Our results highlight an increase in AHUA in adults coinciding with the period of the outbreak in children, but not linked to documented HAdV infection. Tracking changes in routinely collected clinical data through EHR could be used to support outbreak surveillance.

Developing and testing a framework for coding general practitioners' free-text diagnoses in electronic medical records - a reliability study for generating training data in natural language processing.

BACKGROUND: Diagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (NLP) could assist in coding free-text diagnoses, but NLP models require local training data to unlock their potential. The aim of this study was to develop a framework of research-relevant diagnostic codes, to test the framework using free-text diagnoses from a Swiss primary care database and to generate training data for NLP modelling. METHODS: The framework of diagnostic codes was developed based on input from local stakeholders and consideration of epidemiological data. After pre-testing, the framework contained 105 diagnostic codes, which were then applied by two raters who independently coded randomly drawn lines of free text (LoFT) from diagnosis lists extracted from the electronic medical records of 3000 patients of 27 general practitioners. Coding frequency and mean occurrence rates (n and %) and inter-rater reliability (IRR) of coding were calculated using Cohen's kappa (Κ). RESULTS: The sample consisted of 26,980 LoFT and in 56.3% no code could be assigned because it was not a specific diagnosis. The most common diagnostic codes were, 'dorsopathies' (3.9%, a code covering all types of back problems, including non-specific lower back pain, scoliosis, and others) and 'other diseases of the circulatory system' (3.1%). Raters were in almost perfect agreement (Κ ≥ 0.81) for 69 of the 105 diagnostic codes, and 28 codes showed a substantial agreement (K between 0.61 and 0.80). Both high coding frequency and almost perfect agreement were found in 37 codes, including codes that are particularly difficult to identify from components of the electronic medical record, such as musculoskeletal conditions, cancer or tobacco use. CONCLUSION: The coding framework was characterised by a subset of very frequent and highly reliable diagnostic codes, which will be the most valuable targets for training NLP models for automated disease classification based on free-text diagnoses from Swiss general practice.

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study.

BACKGROUND: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. OBJECTIVE: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration's postmarket surveillance capabilities while minimizing public burden. This study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach. METHODS: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV. RESULTS: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively. CONCLUSIONS: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection.

Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review.

BACKGROUND: The rapid progression and integration of digital technologies into public health have reshaped the global landscape of health care delivery and disease prevention. In pursuit of better population health and health care accessibility, many countries have integrated digital interventions into their health care systems, such as web-based consultations, electronic health records, and telemedicine. Despite the increasing prevalence and relevance of digital technologies in public health and their varying definitions, there has been a shortage of studies examining whether these technologies align with the established definition and core characteristics of digital public health (DiPH) interventions. Hence, the imperative need for a scoping review emerges to explore the breadth of literature dedicated to this subject. OBJECTIVE: This scoping review aims to outline DiPH interventions from different implementation stages for health promotion, primary to tertiary prevention, including health care and disease surveillance and monitoring. In addition, we aim to map the reported intervention characteristics, including their technical features and nontechnical elements. METHODS: Original studies or reports of DiPH intervention focused on population health were eligible for this review. PubMed, Web of Science, CENTRAL, IEEE Xplore, and the ACM Full-Text Collection were searched for relevant literature (last updated on October 5, 2022). Intervention characteristics of each identified DiPH intervention, such as target groups, level of prevention or health care, digital health functions, intervention types, and public health functions, were extracted and used to map DiPH interventions. MAXQDA 2022.7 (VERBI GmbH) was used for qualitative data analysis of such interventions' technical functions and nontechnical characteristics. RESULTS: In total, we identified and screened 15,701 records, of which 1562 (9.94%) full texts were considered relevant and were assessed for eligibility. Finally, we included 185 (11.84%) publications, which reported 179 different DiPH interventions. Our analysis revealed a diverse landscape of interventions, with telemedical services, health apps, and electronic health records as dominant types. These interventions targeted a wide range of populations and settings, demonstrating their adaptability. The analysis highlighted the multifaceted nature of digital interventions, necessitating precise definitions and standardized terminologies for effective collaboration and evaluation. CONCLUSIONS: Although this scoping review was able to map characteristics and technical functions among 13 intervention types in DiPH, emerging technologies such as artificial intelligence might have been underrepresented in our study. This review underscores the diversity of DiPH interventions among and within intervention groups. Moreover, it highlights the importance of precise terminology for effective planning and evaluation. This review promotes cross-disciplinary collaboration by emphasizing the need for clear definitions, distinct technological functions, and well-defined use cases. It lays the foundation for international benchmarks and comparability within DiPH systems. Further research is needed to map intervention characteristics in this still-evolving field continuously. TRIAL REGISTRATION: PROSPERO CRD42021265562; https://tinyurl.com/43jksb3k. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/33404.

Using Routinely Collected Electronic Healthcare Record Data to Investigate Fibrotic Multimorbidity in England.

Background: Electronic healthcare records (EHRs) are used to document diagnoses, symptoms, tests, and prescriptions. Though not primarily collected for research purposes, owing to the size of the data as well as the depth of information collected, they have been used extensively to conduct epidemiological research. The Clinical Practice Research Datalink (CPRD) is an EHR database containing representative data of the UK population with regard to age, sex, race, and social deprivation measures. Fibrotic conditions are characterised by excessive scarring, contributing towards organ dysfunction and eventual organ failure. Fibrosis is associated with ageing as well as many other factors, it is hypothesised that fibrotic conditions are caused by the same underlying pathological mechanism. We calculated the prevalence of fibrotic conditions (as defined in a previous Delphi survey of clinicians) as well as the prevalence of fibrotic multimorbidity (the proportion of people with multiple fibrotic conditions). Methods: We included a random sample of 993,370 UK adults, alive, and enrolled at a UK general practice, providing data to the CPRD Aurum database as of 1st of January 2015. Individuals had to be eligible for linkage to hospital episode statistics (HES) and ONS death registration. We calculated the point prevalence of fibrotic conditions and multi-morbid fibrosis on the 1st of January 2015. Using death records of those who died in 2015, we investigated the prevalence of fibrosis associated death. We explored the most commonly co-occurring fibrotic conditions and determined the settings in which diagnoses were commonly made (primary care, secondary care or after death). Results: The point prevalence of any fibrotic condition was 21.46%. In total, 6.00% of people had fibrotic multimorbidity. Of the people who died in 2015, 34.82% had a recording of a fibrotic condition listed on their death certificate. Conclusion: The key finding was that fibrotic multimorbidity affects approximately 1 in 16 people.

Fibrotic conditions are scarring conditions which impact the way an organ functions and eventually lead to organ failure. We studied routinely collected health data from GPs, hospitals, and death certificates to estimate the percentage of UK adults who had fibrotic diseases. We found that 1 in 5 people had at least one fibrotic disease, and we also found that 1 in 16 people had more than one fibrotic disease.

Treatment of hypertension during pregnancy: a cohort of pregnancy episodes from the SIDIAP database, Catalonia, Spain.

Introduction: Hypertension during pregnancy is one of the most frequent causes of maternal and fetal morbimortality. Perinatal and maternal death and disability rates have decreased by 30%, but hypertension during pregnancy has increased by approximately 10% in the last 30 years. This research aimed to describe the pharmacological treatment and pregnancy outcomes of pregnancies with hypertension. Methods: We carried out an observational cohort study from the Information System for the Development of Research in Primary Care (SIDIAP) database. Pregnancy episodes with hypertension (ICD-10 codes for hypertension, I10-I15 and O10-O16) were identified. Antihypertensives were classified according to the ATC WHO classification: ß-blocking agents (BBs), calcium channel blockers (CCBs), agents acting on the renin-angiotensin system (RAS agents), diuretics, and antiadrenergic agents. Exposure was defined for hypertension in pregnancies with ≥2 prescriptions during the pregnancy episode. Descriptive statistics for diagnoses and treatments were calculated. Results: In total, 4,839 pregnancies with hypertension diagnosis formed the study cohort. There were 1,944 (40.2%) pregnancies exposed to an antihypertensive medication. There were differences in mother's age, BMI, and alcohol intake between pregnancies exposed to antihypertensive medications and those not exposed. BBs were the most used (n = 1,160 pregnancy episodes; 59.7%), followed by RAS agents (n = 825, 42.4%), and CCBs were the least used (n = 347, 17.8%). Discussion: Pregnancies involving hypertension were exposed to antihypertensive medications, mostly BBs. We conduct a study focused on RAS agent use during pregnancy and its outcomes in the offspring.

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

Treatment drop-in in a contemporary cohort used to derive cardiovascular risk prediction equations.

BACKGROUND: No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment. METHODS: Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined. RESULTS: A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds). CONCLUSIONS: CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

COPD Exacerbations Before and During COVID-19 in France, Germany, Italy, the UK and the US.

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) were reported less frequently during the COVID-19 pandemic. We report real-world data on COPD exacerbation rates before and during this pandemic. Methods: Exacerbation patterns were analysed using electronic medical records or claims data of patients with COPD before (2017-2019) and during the COVID-19 pandemic (2020 through early 2022) in France, Germany, Italy, the United Kingdom and the United States. Data from each country were analysed separately. The proportions of patients with COPD receiving maintenance treatment were also estimated. Results: The proportion of patients with exacerbations fell 45-78% across five countries in 2020 versus 2019. Exacerbation rates in most countries were reduced by >50% in 2020 compared with 2019. The proportions of patients with an exacerbation increased in most countries in 2021. Across each country, seasonal exacerbation increases seen during autumn and winter in pre-pandemic years were absent during the first year of the pandemic. The percentage of patients filling COPD prescriptions across each country increased by 4.53-22.13% in 2019 to 9.94-34.17% in 2021. Conclusion: Early, steep declines in exacerbation rates occurred in 2020 versus 2019 across all five countries and were accompanied by a loss of the seasonal pattern of exacerbation.

Recurrent urinary tract infections and prophylactic antibiotic use in women: cross-sectional study.

BACKGROUND: Despite the considerable morbidity caused by recurrent UTIs (rUTIs), and the wider personal and public health implications from frequent antibiotic use, few studies adequately describe the prevalence and characteristics of women with rUTIs or those who use prophylactic antibiotics. AIM: To describe the prevalence, characteristics, and urine profiles of women with rUTIs with and without prophylactic antibiotic use in Welsh primary care. DESIGN AND SETTING: Retrospective cross-sectional study in Welsh General Practice using the SAIL Databank. METHOD: We describe the characteristics of women aged ≥18 years with rUTIs or using prophylactic antibiotics from 2010-2020, and associated urine culture results from 2015 - 2020. RESULTS: 6.0% of women (n=92,213) had rUTIs, and 1.7% (n=26,862) were prescribed prophylactic antibiotics. Only 49% of prophylactic antibiotic users met the definition of rUTIs before initiation. 81% of women with rUTIs had a urine culture result in the preceding 12 months with high rates of resistance to trimethoprim and amoxicillin. 64% of women taking prophylactic antibiotics had a urine culture result before initiation, and 18% (n=320) of women prescribed trimethoprim had resistance to it on the antecedent sample. CONCLUSION: A substantial proportion of women had rUTIs or incident prophylactic antibiotic use. However, 64% of women had urine cultured before starting prophylaxis. There was a high proportion of cultured bacteria resistant to two antibiotics used for rUTI prevention and evidence of resistance to the prescribed antibiotic. More frequent urine cultures for rUTI diagnosis and before prophylactic antibiotic initiation could better inform antibiotic choices.

Assessing the quality of emergency department data for injury surveillance in Victoria, Australia: a comparative analysis of two Victorian hospital data sources.

OBJECTIVE: The emergency department (ED) is pivotal in treating serious injuries, making it a valuable source for population-based injury surveillance. In Victoria, information that is relevant to injury surveillance is collected in the Victorian Emergency Minimum Dataset (VEMD). This study aims to assess the data quality of the VEMD as an injury data source by comparing it with the Victorian Admitted Episodes Dataset (VAED). DESIGN: A retrospective observational study of administrative healthcare data. SETTING AND PARTICIPANTS: VEMD and VAED data from July 2014 to June 2019 were compared. Including only hospitals contributing to both datasets, cases that (1) arrived at the ED and (2) were subsequently admitted, were selected. RESULTS: While the overall number of cases was similar, VAED outnumbered VEMD cases (414 630 vs 404 608), suggesting potential under-reporting of injuries in the ED. Age-related differences indicated a relative under-representation of older individuals in the VEMD. Injuries caused by falls or transport, and intentional injuries were relatively under-reported in the VEMD. CONCLUSIONS: Injury cases were more numerous in the VAED than in the VEMD even though the number is expected to be equal based on case selection. Older patients were under-represented in the VEMD; this could partly be attributed to patients being admitted for an injury after they presented to the ED with a non-injury ailment. The patterns of under-representation described in this study should be taken into account in ED-based injury incidence reporting.

Association of patient photographs and reduced retract-and-reorder events.

Background: Wrong-patient order entry (WPOE) is a potentially dangerous medical error. It remains unknown if patient photographs reduce WPOE in the pediatric inpatient population. Materials and Methods: Order sessions from a single pediatric hospital system were examined for retract-and-reorder (RAR) events, a surrogate WPOE measure. We determined the association of patient photographs with the proportion of order sessions resulting in a RAR event, adjusted for patient, provider, and ordering context. Results: In multivariable analysis, the presence of a patient photo in the electronic health record was associated with 40% lower odds of a RAR event (aOR: 0.60, 95% CI: 0.48-0.75), while cardiac and ICU contexts had higher RAR frequency (aOR: 2.12, 95% CI: 1.69-2.67 and 2.05, 95% CI: 1.71-2.45, respectively). Discussion and Conclusion: Patient photos were associated with lower odds of RAR events in the pediatric inpatient setting, while high acuity locations may be at higher risk. Patient photographs may reduce WPOE without interruptions.

Prospective validation of a mobile health application for blood pressure management in patients with hypertensive disorders of pregnancy: study protocol for a randomized controlled trial.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) pose significant risks to both maternal and fetal health, contributing to global morbidity and mortality. Management of HDP is complex, particularly because of concerns regarding potential negative effects on utero-placental circulation and limited therapeutic options due to fetal safety. Our study investigates whether blood pressure monitoring through a mobile health (mHealth) application can aid in addressing the challenges of blood pressure management in pregnant individuals with HDP. Additionally, we aim to assess whether this intervention can improve short-term maternal and fetal outcomes and potentially mitigate long-term cardiovascular consequences. METHODS: This prospective, randomized, single-center trial will include 580 pregnant participants who meet the HDP criteria or who have a heightened risk of pregnancy-related hypertension due to factors such as multiple pregnancies, obesity, diabetes, or a history of HDP in prior pregnancies leading to preterm birth. Participants will be randomized to either the mHealth intervention group or the standard care group. The primary endpoint is the difference in systolic blood pressure from enrollment to 1 month after childbirth. The secondary endpoints include various blood pressure parameters, obstetric outcomes, body mass index trajectory, step counts, mood assessment, and drug adherence. CONCLUSIONS: This study emphasizes the potential of mHealth interventions, such as the Heart4U application, to improve blood pressure management in pregnant individuals with HDP. By leveraging technology to enhance engagement, communication, and monitoring, this study aims to positively impact maternal, fetal, and postpartum outcomes associated with HDP. This innovative approach demonstrates the potential of personalized technology-driven solutions for managing complex health conditions. TRIAL REGISTRATION: ClinicalTrials.gov NCT05995106. Registered on 16 August 2023.

A consensus-based electronic medical record template for pediatric morphea patient visits.

Morphea, also known as localized scleroderma, is an inflammatory sclerosing disorder of uncertain pathogenesis that affects the skin and underlying tissues. In the pediatric population, the disease often runs a chronic course with a high risk for irreversible sequelae; as such, patients often require long-term monitoring. The objective of this study is to develop a multi-center, consensus-based electronic medical record template for pediatric morphea patient visits using a modified Delphi method of iterative surveys. By facilitating consistent data collection and interpretation across medical centers and patient populations, this template may improve patient care for pediatric patients with morphea.

Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions.

In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force. Urogynecology, a discipline anchored in intricate patient histories and meticulous follow-ups, is on the brink of profound transformation due to these digital strides. While EHRs have unified patient data, challenges related to data privacy, interoperability, and access persist. In response, we present Pelvic Health Place (PHPlace) - a multilingual, patient-centric application. Purposefully designed to bolster patient engagement, PHPlace provides clinicians with essential pre-consultation insights, streamlines the consent process, vividly delineates surgical pathways, and assures comprehensive long-term monitoring. This platform also establishes a foundation for global data amalgamation, promising to invigorate research and potentially harness artificial intelligence (AI) capabilities. With AI integration, we anticipate a more tailored treatment approach and enriched patient education, signaling a pivotal shift in urogynecology and emphasizing the imperative for ongoing academic inquiry.

Implementing a Clinical Decision Support Tool to Increase Early Peanut Introduction Guidance.

BACKGROUND: General pediatric providers are the frontline for early peanut introduction discussions, but many feel ill-equipped to handle such discussions as guidelines have quickly changed. OBJECTIVE: We hypothesized that a clinical decision support (CDS) tool could improve peanut introduction discussions. METHODS: CDS tools were designed by stakeholders, improved through usability testing, and integrated into the current note templates. Based on queries of electronic health record (EHR), we did a pre-post performance evaluation of peanut introduction conversations, barriers for introduction, and percentage of 12-month WCC visits that had successfully introduced peanut. Providers completed surveys before and after intervention to assess awareness of early peanut introduction and comfort using CDS. RESULTS: Providers' awareness of early peanut introduction guidelines increased from 17.8% to 66.7% after the CDS tool was implemented. 79.1% were comfortable using the tool. The CDS tool improved peanut introduction conversations at the 4-month well-child (WCC) care visit from 2.4% to 81.2%, at the 6-month WCC visit from 3.0% to 84.2%, and at the 12-month WCC visit from 2.7% to 82.9%. 56.6% of families had a plan to introduce peanut at the 4-month WCC visit. Of those who did not have a plan, the most common barrier was family's unawareness of the benefits of early peanut introduction. At the 12-month visit, 62.8% of families had introduced peanut without concerns. CONCLUSION: A point-of-care CDS tool encouraged more discussions by general pediatric providers on early peanut introduction to all patients. CDS tools should be considered in quality improvement projects as an implementation method for the most up-to-date guidelines.