COVID-19 Computed tomography patterns in renal replacement therapy patients / Padrões de tomografia computadorizada da COVID-19 em pacientes em terapia renal substitutiva

ABSTRACT Introduction: Lung diseases are common in patients with end stage kidney disease (ESKD), making differential diagnosis with COVID-19 a challenge. This study describes pulmonary chest tomography (CT) findings in hospitalized ESKD patients on renal replacement therapy (RRT) with clinical suspicion of COVID-19. Methods: ESKD individuals referred to emergency department older than 18 years with clinical suspicion of COVID-19 were recruited. Epidemiological baseline clinical information was extracted from electronic health records. Pulmonary CT was classified as typical, indeterminate, atypical or negative. We then compared the CT findings of positive and negative COVID-19 patients. Results: We recruited 109 patients (62.3% COVID-19-positive) between March and December 2020, mean age 60 ± 12.5 years, 43% female. The most common etiology of ESKD was diabetes. Median time on dialysis was 36 months, interquartile range = 12-84. The most common pulmonary lesion on CT was ground glass opacities. Typical CT pattern was more common in COVID-19 patients (40 (61%) vs 0 (0%) in non-COVID-19 patients, p < 0.001). Sensitivity was 60.61% (40/66) and specificity was 100% (40/40). Positive predictive value and negative predictive value were 100% and 62.3%, respectively. Atypical CT pattern was more frequent in COVID-19-negative patients (9 (14%) vs 24 (56%) in COVID-19-positive, p < 0.001), while the indeterminate pattern was similar in both groups (13 (20%) vs 6 (14%), p = 0.606), and negative pattern was more common in COVID-19-negative patients (4 (6%) vs 12 (28%), p = 0.002). Conclusions: In hospitalized ESKD patients on RRT, atypical chest CT pattern cannot adequately rule out the diagnosis of COVID-19.

RESUMO Introdução: Doenças pulmonares são comuns em pacientes com doença renal em estágio terminal (DRET), dificultando o diagnóstico diferencial com COVID-19. Este estudo descreve achados de tomografia computadorizada de tórax (TC) em pacientes com DRET em terapia renal substitutiva (TRS) hospitalizados com suspeita de COVID-19. Métodos: Indivíduos maiores de 18 anos com DRET, encaminhados ao pronto-socorro com suspeita de COVID-19 foram incluídos. Dados clínicos e epidemiológicos foram extraídos de registros eletrônicos de saúde. A TC foi classificada como típica, indeterminada, atípica, negativa. Comparamos achados tomográficos de pacientes com COVID-19 positivos e negativos. Resultados: Recrutamos 109 pacientes (62,3% COVID-19-positivos) entre março e dezembro de 2020, idade média de 60 ± 12,5 anos, 43% mulheres. A etiologia mais comum da DRET foi diabetes. Tempo médio em diálise foi 36 meses, intervalo interquartil = 12-84. A lesão pulmonar mais comum foi opacidades em vidro fosco. O padrão típico de TC foi mais comum em pacientes com COVID-19 (40 (61%) vs. 0 (0%) em pacientes sem COVID-19, p < 0,001). Sensibilidade 60,61% (40/66), especificidade 100% (40/40). Valores preditivos positivos e negativos foram 100% e 62,3%, respectivamente. Padrão atípico de TC foi mais frequente em pacientes COVID-19-negativos (9 (14%) vs. 24 (56%) em COVID-19-positivos, p < 0,001), enquanto padrão indeterminado foi semelhante em ambos os grupos (13 (20%) vs. 6 (14%), p = 0,606), e padrão negativo foi mais comum em pacientes COVID-19-negativos (4 (6%) vs. 12 (28%), p = 0,002). Conclusões: Em pacientes com DRET em TRS hospitalizados, um padrão atípico de TC de tórax não pode excluir adequadamente o diagnóstico de COVID-19.

Risk factors for acute kidney injury in patients hospitalized with COVID-19 / Fatores de risco para lesão renal aguda em pacientes hospitalizados com COVID-19

Abstract Introduction: Acute kidney injury (AKI) occurs frequently in COVID-19 patients and is associated with greater morbidity and mortality. Knowing the risks of AKI allows for identification, prevention, and timely treatment. This study aimed to identify the risk factors associated with AKI in hospitalized patients. Methods: A descriptive, retrospective, cross-sectional, and analytical component study of adult patients hospitalized with COVID-19 from March 1 to December 31, 2020 was carried out. AKI was defined by the creatinine criteria of the KDIGO-AKI guidelines. Information, regarding risk factors, was obtained from electronic medical records. Results: Out of the 934 patients, 42.93% developed AKI, 60.59% KDIGO-1, and 9.9% required renal replacement therapy. Patients with AKI had longer hospital stay, higher mortality, and required more intensive care unit (ICU) admission, mechanical ventilation, and vasopressor support. Multivariate analysis showed that age (OR 1.03; 95% CI 1.02-1.04), male sex (OR 2.13; 95% CI 1.49-3.04), diabetes mellitus (DM) (OR 1.55; 95% CI 1.04-2.32), chronic kidney disease (CKD) (OR 2.07; 95% CI 1.06-4.04), C-reactive protein (CRP) (OR 1.02; 95% CI 1.00-1.03), ICU admission (OR 1.81; 95% CI 1.04-3.16), and vasopressor support (OR 7.46; 95% CI 3.34-16.64) were risk factors for AKI, and that bicarbonate (OR 0.89; 95% CI 0.84-0.94) and partial pressure arterial oxygen/inspired oxygen fraction index (OR 0.99; 95% CI 0.98-0.99) could be protective factors. Conclusions: A high frequency of AKI was documented in COVID-19 patients, with several predictors: age, male sex, DM, CKD, CRP, ICU admission, and vasopressor support. AKI occurred more frequently in patients with higher disease severity and was associated with higher mortality and worse outcomes.

RESUMO Introdução: Lesão renal aguda (LRA) ocorre frequentemente em pacientes com COVID-19 e associa-se a maior morbidade e mortalidade. Conhecer riscos da LRA permite a identificação, prevenção e tratamento oportuno. Este estudo teve como objetivo identificar fatores de risco associados à LRA em pacientes hospitalizados. Métodos: Realizou-se estudo descritivo, retrospectivo, transversal e de componente analítico de pacientes adultos hospitalizados com COVID-19 de 1º de março a 31 de dezembro, 2020. Definiu-se a LRA pelos critérios de creatinina das diretrizes KDIGO-LRA. Informações sobre fatores de risco foram obtidas de prontuários eletrônicos. Resultados: Dos 934 pacientes, 42,93% desenvolveram LRA, 60,59% KDIGO-1 e 9,9% necessitaram de terapia renal substitutiva. Pacientes com LRA apresentaram maior tempo de internação, maior mortalidade e necessitaram de mais internações em UTIs, ventilação mecânica e suporte vasopressor. A análise multivariada mostrou que idade (OR 1,03; IC 95% 1,02-1,04), sexo masculino (OR 2,13; IC 95% 1,49-3,04), diabetes mellitus (DM) (OR 1,55; IC 95% 1,04-2,32), doença renal crônica (DRC) (OR 2,07; IC 95% 1,06-4,04), proteína C reativa (PCR) (OR 1,02; IC 95% 1,00-1,03), admissão em UTI (OR 1,81; IC 95% 1,04-3,16) e suporte vasopressor (OR 7,46; IC 95% 3,34-16,64) foram fatores de risco para LRA, e que bicarbonato (OR 0,89; IC 95% 0,84-0,94) e índice de pressão parcial de oxigênio arterial/fração inspirada de oxigênio (OR 0,99; IC 95% 0,98-0,99) poderiam ser fatores de proteção. Conclusões: Documentou-se alta frequência de LRA em pacientes com COVID-19, com diversos preditores: idade, sexo masculino, DM, DRC, PCR, admissão em UTI e suporte vasopressor. LRA ocorreu mais frequentemente em pacientes com maior gravidade da doença e associou-se a maior mortalidade e piores desfechos.

Potentially inappropriate prescribing for people with dementia in ambulatory care: a cross-sectional observational study.

BACKGROUND: Studies have shown that potentially inappropriate prescribing (PIP) is highly prevalent among people with dementia (PwD) and linked to negative outcomes, such as hospitalisation and mortality. However, there are limited data on prescribing appropriateness for PwD in Saudi Arabia. Therefore, we aimed to estimate the prevalence of PIP and investigate associations between PIP and other patient characteristics among PwD in an ambulatory care setting. METHODS: A cross-sectional, retrospective analysis was conducted at a tertiary hospital in Saudi Arabia. Patients who were ≥ 65 years old, had dementia, and visited ambulatory care clinics between 01/01/2019 and 31/12/2021 were included. Prescribing appropriateness was evaluated by applying the Screening Tool of Older Persons Potentially Inappropriate Prescriptions (STOPP) criteria. Descriptive analyses were used to describe the study population. Prevalence of PIP and the prevalence per each STOPP criterion were calculated as a percentage of all eligible patients. Logistic regression analysis was used to investigate associations between PIP, polypharmacy, age and sex; odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Analyses were conducted using SPSS v27. RESULTS: A total of 287 PwD were identified; 56.0% (n = 161) were female. The mean number of medications prescribed was 9.0 [standard deviation (SD) ± 4.2]. The prevalence of PIP was 61.0% (n = 175). Common instances of PIP were drugs prescribed beyond the recommended duration (n = 90, 31.4%), drugs prescribed without an evidence-based clinical indication (n = 78, 27.2%), proton pump inhibitors (PPIs) for > 8 weeks (n = 75, 26.0%), and acetylcholinesterase inhibitors with concurrent drugs that reduce heart rate (n = 60, 21.0%). Polypharmacy was observed in 82.6% (n = 237) of patients and was strongly associated with PIP (adjusted OR 24.1, 95% CI 9.0-64.5). CONCLUSIONS: Findings have revealed a high prevalence of PIP among PwD in Saudi Arabia that is strongly associated with polypharmacy. Future research should aim to explore key stakeholders' experiences and perspectives of medicines management to optimise medication use for this vulnerable patient population.

Bronchiectasis-associated infections and outcomes in a large, geographically diverse electronic health record cohort in the United States.

BACKGROUND: Bronchiectasis is a pulmonary disease characterized by irreversible dilation of the bronchi and recurring respiratory infections. Few studies have described the microbiology and prevalence of infections in large patient populations outside of specialized tertiary care centers. METHODS: We used the Cerner HealthFacts Electronic Health Record database to characterize the nature, burden, and frequency of pulmonary infections among persons with bronchiectasis. Chronic infections were defined based on organism-specific guidelines. RESULTS: We identified 7,749 patients who met our incident bronchiectasis case definition. In this study population, the organisms with the highest rates of isolate prevalence were Pseudomonas aeruginosa with 937 (12%) individuals, Staphylococcus aureus with 502 (6%), Mycobacterium avium complex (MAC) with 336 (4%), and Aspergillus sp. with 288 (4%). Among persons with at least one isolate of each respective pathogen, 219 (23%) met criteria for chronic P. aeruginosa colonization, 74 (15%) met criteria for S. aureus chronic colonization, 101 (30%) met criteria for MAC chronic infection, and 50 (17%) met criteria for Aspergillus sp. chronic infection. Of 5,795 persons with at least two years of observation, 1,860 (32%) had a bronchiectasis exacerbation and 3,462 (60%) were hospitalized within two years of bronchiectasis diagnoses. Among patients with chronic respiratory infections, the two-year occurrence of exacerbations was 53% and for hospitalizations was 82%. CONCLUSIONS: Patients with bronchiectasis experiencing chronic respiratory infections have high rates of hospitalization.

Surveillance of catheter-associated bloodstream infections: development and validation of a fully automated algorithm.

BACKGROUND: Most surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU). METHODS: We developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital's data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period. RESULTS: From January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50-70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06-0.41) and 3.86/1000 catheter days (95% CI: 3.17-4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7-96.9), 100% (95% CI 99.5-100), 100% (95% CI 56.5-100), and 99.9% (95% CI 99.2-100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm. CONCLUSIONS: A fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.

Implementation of COVID-19 vaccination services in prison in six European countries: translating emergency intervention into routine life-course vaccination.

BACKGROUND: Evidence has shown that the risk of transmission of SARS-CoV-2 is much higher in prisons than in the community. The release of the COVID-19 vaccine and the recommendation by WHO to include prisons among priority settings have led to the inclusion of prisons in national COVID-19 vaccination strategies. Evidence on prison health and healthcare services provision is limited and often focuses on a single country or institution due to the multiple challenges of conducting research in prison settings. The present study was done in the framework of the EU-founded project RISE-Vac. It aimed to analyse the best practices and challenges applied in implementing COVID-19 universal vaccination services during the pandemic to support future expansion of routine life course vaccination services for people living in prison (PLP). METHODS: Two online cross-sectional surveys were designed and piloted: survey1 on prison characteristics and (non-COVID-19) immunisation practices; survey2 on the implementation and coverage of COVID-19 vaccination with open-ended questions for thematic analysis. Each RISE-Vac project partner distributed the questionnaire to one or two prisons in their country. Answers were collected from eight European prisons' directors or medical directors between November 2021-May 2022. RESULTS: According to our findings, the implementation modalities of COVID-19 vaccination services in the surveyed prisons were effective in improving PLP vaccination coverage. Strategies for optimal management of the vaccination campaign included: periodic time slot for PLP vaccination; new staff recruitment and task shifting; distribution of informational material both to PLP and prison staff. Key challenges included continuity of care after release, immunisation information system, and vaccine hesitancy. CONCLUSIONS: To the best of our knowledge, this is the first study describing the implementation of COVID-19 vaccination services in European prisons, suggesting that the expansion of vaccination provision in prison is possible. There is no unique solution that will fit every country but commonalities likely to be important in the design and implementation of future vaccination campaigns targeting PLP emerged. Increased availability of vaccination services in prison is not only possible, but feasible and highly desirable, and can contribute to the reduction of health inequalities.

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST.

BACKGROUND: The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.79 (SD 0.23) as of 2019 and approaching the benchmark for well-documented reports (0.80). However, the contributing factors to this relatively high report completeness score remain unexplored. OBJECTIVE: This study aims to explore the main drivers influencing the completeness of Malaysian AE reports in VigiBase over a 15-year period using vigiGrade. A secondary objective was to understand the strategic measures taken by the Malaysian authorities leading to enhanced report completeness across different time frames. METHODS: We analyzed 132,738 Malaysian reports (2005-2019) recorded in VigiBase up to February 2021 split into historical International Drug Information System (INTDIS; n=63,943, 48.17% in 2005-2016) and newer E2B (n=68,795, 51.83% in 2015-2019) format subsets. For machine learning analyses, we performed a 2-stage feature selection followed by a random forest classifier to identify the top features predicting well-documented reports. We subsequently applied tree Shapley additive explanations to examine the magnitude, prevalence, and direction of feature effects. In addition, we conducted time-series analyses to evaluate chronological trends and potential influences of key interventions on reporting quality. RESULTS: Among the analyzed reports, 42.84% (56,877/132,738) were well documented, with an increase of 65.37% (53,929/82,497) since 2015. Over two-thirds (46,186/68,795, 67.14%) of the Malaysian E2B reports were well documented compared to INTDIS reports at 16.72% (10,691/63,943). For INTDIS reports, higher pharmacovigilance center staffing was the primary feature positively associated with being well documented. In recent E2B reports, the top positive features included reaction abated upon drug dechallenge, reaction onset or drug use duration of <1 week, dosing interval of <1 day, reports from public specialist hospitals, reports by pharmacists, and reaction duration between 1 and 6 days. In contrast, reports from product registration holders and other health care professionals and reactions involving product substitution issues negatively affected the quality of E2B reports. Multifaceted strategies and interventions comprising policy changes, continuity of education, and human resource development laid the groundwork for AE reporting in Malaysia, whereas advancements in technological infrastructure, pharmacovigilance databases, and reporting tools concurred with increases in both the quantity and quality of AE reports. CONCLUSIONS: Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.

Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study.

BACKGROUND: The rehabilitation of a patient who had a stroke requires precise, personalized treatment plans. Natural language processing (NLP) offers the potential to extract valuable exercise information from clinical notes, aiding in the development of more effective rehabilitation strategies. OBJECTIVE: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of patients who had a stroke treated at the University of Pittsburgh Medical Center. METHODS: A cohort of 13,605 patients diagnosed with stroke was identified, and their clinical notes containing rehabilitation therapy notes were retrieved. A comprehensive clinical ontology was created to represent various aspects of physical rehabilitation exercises. State-of-the-art NLP algorithms were then developed and compared, including rule-based, machine learning-based algorithms (support vector machine, logistic regression, gradient boosting, and AdaBoost) and large language model (LLM)-based algorithms (ChatGPT [OpenAI]). The study focused on key performance metrics, particularly F1-scores, to evaluate algorithm effectiveness. RESULTS: The analysis was conducted on a data set comprising 23,724 notes with detailed demographic and clinical characteristics. The rule-based NLP algorithm demonstrated superior performance in most areas, particularly in detecting the "Right Side" location with an F1-score of 0.975, outperforming gradient boosting by 0.063. Gradient boosting excelled in "Lower Extremity" location detection (F1-score: 0.978), surpassing rule-based NLP by 0.023. It also showed notable performance in the "Passive Range of Motion" detection with an F1-score of 0.970, a 0.032 improvement over rule-based NLP. The rule-based algorithm efficiently handled "Duration," "Sets," and "Reps" with F1-scores up to 0.65. LLM-based NLP, particularly ChatGPT with few-shot prompts, achieved high recall but generally lower precision and F1-scores. However, it notably excelled in "Backward Plane" motion detection, achieving an F1-score of 0.846, surpassing the rule-based algorithm's 0.720. CONCLUSIONS: The study successfully developed and evaluated multiple NLP algorithms, revealing the strengths and weaknesses of each in extracting physical rehabilitation exercise information from clinical notes. The detailed ontology and the robust performance of the rule-based and gradient boosting algorithms demonstrate significant potential for enhancing precision rehabilitation. These findings contribute to the ongoing efforts to integrate advanced NLP techniques into health care, moving toward predictive models that can recommend personalized rehabilitation treatments for optimal patient outcomes.

Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024.

BACKGROUND: Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO's mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. OBJECTIVE: We aim to describe the RSC's plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. METHODS: Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA's sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA's reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. RESULTS: We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC's pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. CONCLUSIONS: The RSC extended its surveillance activities to meet more but not all of the mosaic framework's objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies.

Risk of diabetes and expected years in life without diabetes among adults from an urban community in India: findings from a retrospective cohort.

BACKGROUND: Diabetes prevalence has increased over the past few decades, and the shift of the burden of diabetes from the older population to the younger population has increased the exposure of longer durations in a morbid state. The study aimed at ascertaining the likelihood of progression to diabetes and to estimate the onset of diabetes within the urban community of Mumbai. METHODS: This study utilized an observational retrospective non-diabetic cohort comprising 1629 individuals enrolled in a health security scheme. Ten years of data were extracted from electronic medical records, and the life table approach was employed to assess the probability of advancing to diabetes and estimate the expected number of years lived without a diabetes diagnosis. RESULTS: The study revealed a 42% overall probability of diabetes progression, with age and gender variations. Males (44%) show higher probabilities than females (40%) of developing diabetes. Diabetes likelihood rises with age, peaking in males aged 55-59 and females aged 65-69. Males aged 30-34 exhibit a faster progression (10.6 years to diagnosis) compared to females (12.3 years). CONCLUSION: The study's outcomes have significant implications for the importance of early diabetes detection. Progression patterns suggest that younger cohorts exhibit a comparatively slower rate of progression compared to older cohorts.

Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.

BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. METHODS: Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy. RESULTS: The trained models' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%). CONCLUSIONS: BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors.

OBJECTIVE: To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors. METHODS: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset. RESULTS: An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis.

This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.

Clinicians' Attitudes Toward Electronic Health Records in Saudi Arabia.

AIM:  This study explored physicians' and nurses' attitudes toward an electronic health record (EHR) system and examined the features and factors that clinicians associated with the implementation of EHR systems. METHODS:  A self-administered anonymous questionnaire with high reliability and validity was adopted from existing research to gather clinicians' attitudes toward the EHR system implemented at King Khalid University Hospital, one of the biggest hospitals in Riyadh, Saudi Arabia. RESULTS:  A total of 438 questionnaire responses were received from the participants; 240 of them were physicians and 198 were nurses. The participants had a mean age of 43.7 years (standard deviation (SD) 17.1), 213 (52.7%) were female and 207 (47.3%) were male. Most participants (424, 96.8%) had one or more years of experience using computers, and a majority (304, 69.4%) had one or more years of experience using EHR systems. Most physicians and nurses (214, 89.5% vs. 174, 87.9%) were satisfied with their hospital's EHR system and felt that the system was highly usable and had the potential to improve communication between staff, facilitate easy storage of and access to information and lead to improved health outcomes for patients. The study found positive attitudes among clinicians concerning the quality of training and education around the new system (178, 74.2% of physicians vs. 142, 71.7% of nurses; p > 0.05) and toward leadership during the transition to HER (222, 92.5% vs. 183, 92.4%). On the other hand, a majority of nurses reported that the EHR system took longer to use and increased their workload compared with the previous analogue system (115 (47.9%) vs. 133 (67.2%); p ≤ 0.01 and 46.7% vs. 112 (64.1%)). A large majority of physicians and nurses surveyed (214 (89.2%) vs. 167 (84.3%)) stated that clinicians should be consulted in the design of such systems as a way to maximise the potential benefits of EHR and mitigate extra workload demands. CONCLUSION:  Most clinicians expressed overall satisfaction with the EHR system, but there were some areas of dissatisfaction among the respondents, such as increasing workload and stress among nurses. There is scope for further research to continue to explore physicians' and nurses' attitudes toward EHRs and for future experimental studies that examine the impact of EHRs on clinician workloads, patient health outcomes and quality of care.

The burden and management competency of cardiomyopathies in China: a nationwide survey study.

Background: The public health burden of cardiomyopathies and competency in their management by health agencies in China are not well understood. Methods: This study adopted a multi-stage sampling method for hospital selection. In the first stage, nationwide tertiary hospital recruitment was performed. As a result, 88 hospitals with the consent of the director of cardiology and access to an established electronic medical records system, were recruited. In the second stage, we sampled 66 hospitals within each geographic-economic stratification through a random sampling process. Data on (1) the outpatient and inpatient visits for cardiomyopathies between 2017 and 2021 and (2) the competency in the management of patients with cardiomyopathies, were collected. The competency of a hospital to provide cardiomyopathy care was evaluated using a specifically devised scale. Findings: The outpatient and inpatient visits for cardiomyopathies increased between 2017 and 2021 by 38.6% and 33.0%, respectively. Most hospitals had basic facilities for cardiomyopathy assessment. However, access to more complex procedures was limited, and the integrated management pathway needs improvement. Only 4 (6.1%) of the 66 participating hospitals met the criteria for being designated as a comprehensive cardiomyopathy center, and only 29 (43.9%) could be classified as a primary cardiomyopathy center. There were significant variations in competency between hospitals with different administrative and economic levels. Interpretation: The health burden of cardiomyopathies has increased significantly between 2017 and 2021 in China. Although most tertiary hospitals in China can offer basic cardiomyopathy care, more advanced facilities are not yet universally available. Moreover, inconsistencies in the management of cardiomyopathies across hospitals due to differing administrative and economic levels warrants a review of the nation allocation of medical resources. Funding: This work was supported by the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2023-I2M-1-001) and the National High Level Hospital Clinical Research Funding (2022-GSP-GG-17).

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study.

Background: Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods: In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98-99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings: Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38-67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4-26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. Interpretation: The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Funding: Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.

Does WASH FIT improve water, sanitation, and hygiene and related health impacts in healthcare facilities? A systematic review.

Introduction: Environmental health services (e.g., water, sanitation, hygiene, cleaning, waste management) in healthcare facilities are important to improve health outcomes and strengthen health systems, but coverage gaps remain. The World Health Organization and United Nations Children's Fund developed WASH FIT, a quality improvement tool, to help assess and improve environmental health services. Fifty-three countries have adopted it. However, there is little evidence of its effectiveness. This systematic review evaluates whether WASH FIT improves environmental health services or associated health outcomes and impacts. Methods: We conducted database searches to identify relevant studies and extracted data on study design, healthcare facility characteristics, and inputs, activities, outputs, outcomes, and impacts associated with WASH FIT. We summarized the findings using a logic model framework and narrative synthesis. Results: We included 31 studies in the review. Most inputs and activities were described qualitatively. Twenty-three studies reported quantitative outputs, primary WASH FIT indicator scores, and personnel trained on WASH FIT. Nine studies reported longitudinal data demonstrating changes in these outputs throughout WASH FIT implementation. Six studies reported quantitative outcomes measurements; the remainder described outcomes qualitatively or not at all. Common outcomes included allocated funding for environmental health services, community engagement, and government collaboration, changes in knowledge, attitudes, or practices among healthcare staff, patients, or community members, and policy changes. No studies directly measured impacts or evaluated WASH FIT against a rigorous control group. Conclusions: Available evidence is insufficient to evaluate WASH FIT's effects on outputs, outcomes, and impacts. Further effort is needed to comprehensively identify the inputs and activities required to implement WASH FIT and to draw specific links between changes in outputs, outcomes, and impacts. Short-term opportunities exist to improve evidence by more comprehensive reporting of WASH FIT assessments and exploiting data on health impacts within health management information systems. In the long term, we recommend experimental studies. This evidence is important to ensure that funding invested for WASH FIT implementation is used cost-effectively and that opportunities to adapt and refine WASH FIT are fully realized as it continues to grow in use and influence.

Utilisation and impact of predict prostate on decision-making among clinicians and patients in a specialist tertiary referral centre: A retrospective cohort study.

Background: Patients with intermediate-risk prostate cancer are faced with the decision of whether to undergo radical treatment. Decision-making aids, such as Predict Prostate, can empower both clinicians and patients to make treatment decisions with personalised information, but their impact on multi-disciplinary team (MDT) decision-making and uptake of radical treatment remains unknown. Objective: The objective of this study is to assess the utilisation and utility of Predict Prostate in informing treatment decisions for patients with intermediate-risk prostate cancer. Patients and Methods: A retrospective cohort study was conducted in Cambridge University Hospitals (CUH) of patients referred to the prostate cancer specialist multi-disciplinary team (pcSMDT) and robotic prostatectomy clinic (ROPD) between September 2019 and August 2021 for consideration of radical prostatectomy (RARP). Data on patient characteristics, use of PredictProstate and management decisions were collected from the Epic electronic medical record (EMR) of 839 patients, of whom 386 had intermediate-risk prostate cancer. Results: The use of Predict Prostate at the pcSMDT increased in the second half of the study period (34.5% vs. 23.8%, p < 0.001). The use of Predict Prostate was associated with an increased likelihood of attending ROPD for men with CPG2 prostate cancer (OR = 2.155, 95% CI = 1.158-4.013, p = 0.015) but a reduced likelihood of proceeding with RARP for men with CPG2 (OR = 0.397, 95% CI = 0.209-0.753, p = 0.005) and CPG3 (OR = 0.305, 95% CI = 0.108-0.861, p = 0.025) prostate cancer. Conclusion: Our study showed that the use of Predict Prostate for patients with intermediate-risk prostate cancer is associated with increased attendance at specialist surgical clinic and a reduced chance of undergoing radical prostate surgery.

Effect of Performance-Based Nonfinancial Incentives on Data Quality in Individual Medical Records of Institutional Births: Quasi-Experimental Study.

BACKGROUND: Despite the potential of routine health information systems in tackling persistent maternal deaths stemming from poor service quality at health facilities during and around childbirth, research has demonstrated their suboptimal performance, evident from the incomplete and inaccurate data unfit for practical use. There is a consensus that nonfinancial incentives can enhance health care providers' commitment toward achieving the desired health care quality. However, there is limited evidence regarding the effectiveness of nonfinancial incentives in improving the data quality of institutional birth services in Ethiopia. OBJECTIVE: This study aimed to evaluate the effect of performance-based nonfinancial incentives on the completeness and consistency of data in the individual medical records of women who availed institutional birth services in northwest Ethiopia. METHODS: We used a quasi-experimental design with a comparator group in the pre-post period, using a sample of 1969 women's medical records. The study was conducted in the "Wegera" and "Tach-armacheho" districts, which served as the intervention and comparator districts, respectively. The intervention comprised a multicomponent nonfinancial incentive, including smartphones, flash disks, power banks, certificates, and scholarships. Personal records of women who gave birth within 6 months before (April to September 2020) and after (February to July 2021) the intervention were included. Three distinct women's birth records were examined: the integrated card, integrated individual folder, and delivery register. The completeness of the data was determined by examining the presence of data elements, whereas the consistency check involved evaluating the agreement of data elements among women's birth records. The average treatment effect on the treated (ATET), with 95% CIs, was computed using a difference-in-differences model. RESULTS: In the intervention district, data completeness in women's personal records was nearly 4 times higher (ATET 3.8, 95% CI 2.2-5.5; P=.02), and consistency was approximately 12 times more likely (ATET 11.6, 95% CI 4.18-19; P=.03) than in the comparator district. CONCLUSIONS: This study indicates that performance-based nonfinancial incentives enhance data quality in the personal records of institutional births. Health care planners can adapt these incentives to improve the data quality of comparable medical records, particularly pregnancy-related data within health care facilities. Future research is needed to assess the effectiveness of nonfinancial incentives across diverse contexts to support successful scale-up.

The unique identifier as one of the essential tools for steering universal health coverage: The case of Morocco / L'identifiant unique comme un des instruments indispensables au pilotage de la couverture sanitaire universelle, le cas du Maroc.

INTRODUCTION: Morocco is carrying out several actions to generalize basic compulsory health insurance (CHI). Managing this project requires coordination, information sharing, and the commitment of all actors to the goal of covering an additional 22 million people. One of the key factors for achieving this objective is the implementation of a unified registration system. PURPOSE OF THE RESEARCH: The aim is to analyze the existing situation and the feasibility of implementing a unified registration system, and to describe the potential positive impact of the latter on the extension of CHI. RESULTS: This work is based on a diagnosis of the current situation. It draws on the legal framework, all available documents and figures, and on an analytical reading supported by existing literature. It reveals that due to the inadequacy or even the absence of an appropriate legal basis, each managing body has its own registration system. The lack of a unified system has given rise to a number of constraints. These concern, among other things: (i) mobility between or within schemes, which does not operate smoothly because it leads to re-registration (ii) inadequate monitoring of double benefit claims, which is the case for more than one scheme, due to insufficient and hesitant anti-fraud action (iii) the sharing and use of reliable data, which hinders decision making, evaluation, and monitoring. CONCLUSIONS: It is essential to adopt legal texts that will provide the basis for a unified system with regulations enabling the participation of all stakeholders, with the aim of steering the roll-out of CHI effectively and efficiently.

Introduction: Le Maroc mène, depuis quelques années, plusieurs actions permettant de généraliser l'assurance maladie obligatoire (AMO). Le pilotage de ce chantier nécessite la coordination, le partage d'informations et l'engagement de tous les acteurs afin de couvrir 22 millions de personnes supplémentaires. L'un des éléments clés pour optimiser la réalisation de cet objectif consiste à mettre en place un système unifié d'immatriculation. But de l'étude: Analyser l'existant et la faisabilité de la mise en place d'un système unifié d'immatriculation, tout en précisant ses retombées positives sur l'extension de l'AMO. Résultats: Ce travail, fondé sur un diagnostic, appuyé par l'arsenal juridique, des documents et des chiffres disponibles ainsi qu'une lecture analytique renforcée par la littérature existante, a permis de constater que, du fait de l'insuffisance voire l'absence d'un soubassement juridique adapté, chaque organisme gestionnaire a son propre système d'immatriculation. L'absence d'un système unifié gêne notamment : 1) la mobilité entre régimes ou intra-régimes, étant donné qu'elle ne se fait pas de manière fluide car elle génère la ré-immatriculation ; 2) le contrôle du double bénéfice d'un régime insuffisamment organisé et incapable de lutter contre la fraude ; 3) le partage et l'exploitation de données fiables empêchant d'assurer de manière appropriée le suivi, l'évaluation et la prise de décision. Conclusion: Il est indispensable d'adopter des textes juridiques pour fonder un système unifié qui permettra l'encadrement et l'engagement de toutes les parties prenantes dans l'objectif de piloter la généralisation de l'AMO avec efficacité et efficience.