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Streaming data routinely generated by social networks, mobile or web applications, e‐commerce, and electronic health records present new opportunities to monitor the impact of an intervention on an outcome via causal inference methods. However, most existing causal inference methods have been focused on and applied to static data, that is, a fixed data set in which observations are pooled and stored before performing statistical analysis. There is thus a pressing need to turn static causal inference into online causal learning to support near real‐time monitoring of treatment effects. In this paper, we present a framework for online estimation and inference of treatment effects that can incorporate new information as it becomes available without revisiting prior observations. We show that, under mild regularity conditions, the proposed online estimator is asymptotically equivalent to the offline oracle estimator obtained by pooling all data. Our proposal is motivated by the need for near real‐time vaccine effectiveness and safety monitoring, and our proposed method is applied to a case study on COVID‐19 vaccine safety surveillance. [ABSTRACT FROM AUTHOR]

Background: Necrotizing fasciitis (NF) is a rare but potentially life-threatening soft tissue infection. The objective of this study was to assess the association between timely surgery within 6 h and hospital mortality in patients with limb NF, and to describe the trends in patients with NF, time to surgery and standardized mortality ratio (SMR) over 11 years. Methods: This was a multicenter, retrospective cohort study of all intensive care unit patients who had emergency surgery within 24 h of hospitalization for limb NF between April 1, 2008 and March 31, 2019 in Hong Kong. Timely surgery was defined as the first surgical treatment within 6 h of initial hospitalization. Appropriate antibiotics were achieved if the patient was given antibiotic(s) for all documented pathogens prior to or on day of culture results. The primary outcome was hospital mortality. Results: There were 495 patients (median age 62 years, 349 (70.5%) males) with limb NF treated by surgery within 24 h of hospitalization over the 11 years. Appropriate antibiotic(s) were used in 392 (79.2%) patients. There were 181 (36.5%) deaths. Timely surgery was not associated with hospital mortality (Relative Risk 0.89, 95% CI: 0.73 to 1.07) but admission year, advanced age, higher severity of illness, comorbidities, renal replacement therapy, vasopressor use, and type of surgery were significant predictors in the multivariable model. There was an upward trend in NF diagnosis (1.9 cases/year, 95% CI: 0.7 to 3.1; P < 0.01; R2 = 0.60) but there was no downward trend in median time to surgery (-0.2 h/year, 95% CI: -0.4 to 0.1; P = 0.16) or SMR (-0.02/year, 95% CI: -0.06 to 0.01; P = 0.22; R2 = 0.16). Conclusions: Among patients operated within 24 h, very early surgery within 6–12 h was not associated with survival. Increasing limb NF cases were reported each year but mortality remained high despite a high rate of appropriate antibiotic use and timely surgical intervention. [ABSTRACT FROM AUTHOR]

Background: Long COVID potentially increases healthcare utilisation and costs. However, its impact on the NHS remains to be determined. Methods: This study aims to assess the healthcare utilisation of individuals with long COVID. With the approval of NHS England, we conducted a matched cohort study using primary and secondary care data via OpenSAFELY, a platform for analysing anonymous electronic health records. The long COVID exposure group, defined by diagnostic codes, was matched with five comparators without long COVID between Nov 2020 and Jan 2023. We compared their total healthcare utilisation from GP consultations, prescriptions, hospital admissions, A&E visits, and outpatient appointments. Healthcare utilisation and costs were evaluated using a two-part model adjusting for covariates. Using a difference-in-difference model, we also compared healthcare utilisation after long COVID with pre-pandemic records. Results: We identified 52,988 individuals with a long COVID diagnosis, matched to 264,867 comparators without a diagnosis. In the 12 months post-diagnosis, there was strong evidence that those with long COVID were more likely to use healthcare resources (OR: 8.29, 95% CI: 7.74–8.87), and have 49% more healthcare utilisation (RR: 1.49, 95% CI: 1.48–1.51). Our model estimated that the long COVID group had 30 healthcare visits per year (predicted mean: 29.23, 95% CI: 28.58–29.92), compared to 16 in the comparator group (predicted mean visits: 16.04, 95% CI: 15.73–16.36). Individuals with long COVID were more likely to have non-zero healthcare expenditures (OR = 7.66, 95% CI = 7.20–8.15), with costs being 44% higher than the comparator group (cost ratio = 1.44, 95% CI: 1.39–1.50). The long COVID group costs approximately £2500 per person per year (predicted mean cost: £2562.50, 95% CI: £2335.60–£2819.22), and the comparator group costs £1500 (predicted mean cost: £1527.43, 95% CI: £1404.33–1664.45). Historically, individuals with long COVID utilised healthcare resources more frequently, but their average healthcare utilisation increased more after being diagnosed with long COVID, compared to the comparator group. Conclusions: Long COVID increases healthcare utilisation and costs. Public health policies should allocate more resources towards preventing, treating, and supporting individuals with long COVID. [ABSTRACT FROM AUTHOR]

Background: Early identification and treatment of chronic disease is associated with better clinical outcomes, lower costs, and reduced hospitalisation. Primary care is ideally placed to identify patients at risk of, or in the early stages of, chronic disease and to implement prevention and early intervention measures. This paper evaluates the implementation of a technological intervention called Future Health Today that integrates with general practice EMRs to (1) identify patients at-risk of, or with undiagnosed or untreated, chronic kidney disease (CKD), and (2) provide guideline concordant recommendations for patient care. The evaluation aimed to identify the barriers and facilitators to successful implementation. Methods: Future Health Today was implemented in 12 general practices in Victoria, Australia. Fifty-two interviews with 30 practice staff were undertaken between July 2020 and April 2021. Practice characteristics were collected directly from practices via survey. Data were analysed using inductive and deductive qualitative analysis strategies, using Clinical Performance - Feedback Intervention Theory (CP-FIT) for theoretical guidance. Results: Future Health Today was acceptable, user friendly and useful to general practice staff, and supported clinical performance improvement in the identification and management of chronic kidney disease. CP-FIT variables supporting use of FHT included the simplicity of design and delivery of actionable feedback via FHT, good fit within existing workflow, strong engagement with practices and positive attitudes toward FHT. Context variables provided the main barriers to use and were largely situated in the external context of practices (including pressures arising from the COVID-19 pandemic) and technical glitches impacting installation and early use. Participants primarily utilised the point of care prompt rather than the patient management dashboard due to its continued presence, and immediacy and relevance of the recommendations on the prompt, suggesting mechanisms of compatibility, complexity, actionability and credibility influenced use. Most practices continued using FHT after the evaluation phase was complete. Conclusions: This study demonstrates that FHT is a useful and acceptable software platform that provides direct support to general practice in identifying and managing patients with CKD. Further research is underway to explore the effectiveness of FHT, and to expand the conditions on the platform. [ABSTRACT FROM AUTHOR]

Background: Understanding the relative contributions of SARS-CoV-2 infection-induced and vaccine-induced seroprevalence is key to measuring overall population-level seroprevalence and help guide policy decisions. Methods: Using a series of six population-based cross-sectional surveys conducted among persons aged ≥7 years in a large health system with over 4.5 million members between May 2021 and April 2022, we combined data from the electronic health record (EHR), an electronic survey and SARS-CoV-2 spike antibody binding assay, to assess the relative contributions of infection and vaccination to population-level SARS-CoV-2 seroprevalence. EHR and survey data were incorporated to determine spike antibody positivity due to SARS-CoV-2 infection and COVID-19 vaccination. We used sampling and non-response weighting to create population-level estimates. Results: We enrolled 4,319 persons over six recruitment waves. SARS-CoV-2 spike antibody seroprevalence increased from 83.3% (CI 77.0–88.9) in May 2021 to 93.5% (CI 89.5–97.5) in April 2022. By April 2022, 68.5% (CI 61.9–74.3) of the population was seropositive from COVID-19 vaccination only, 13.9% (10.7–17.9) from COVID-19 vaccination and prior diagnosed SARS-CoV-2 infection, 8.2% (CI 4.5–14.5) from prior diagnosed SARS-CoV-2 infection only and 2.9% (CI 1.1–7.6) from prior undiagnosed SARS-CoV-2 infection only. We found high agreement (≥97%) between EHR and survey data for ascertaining COVID-19 vaccination and SARS-CoV-2 infection status. Conclusions: By April 2022, 93.5% of persons had detectable SARS-CoV-2 spike antibody, predominantly from COVID-19 vaccination. In this highly vaccinated population and over 18 months into the pandemic, SARS-CoV-2 infection without COVID-19 vaccination was a small contributor to overall population-level seroprevalence. [ABSTRACT FROM AUTHOR]

Background: Individuals facing socioeconomic hardship experience higher than average rates of chronic disease, such as diabetes, with less access to evidence-based treatment. One solution to address these inequities is a team-based care (TBC) model, defined as one in which at least two providers work collaboratively with a patient and their caregiver(s) to make healthcare decisions. This paper seeks to describe the implementation of a TBC model within a safety-net healthcare setting and determine the extent to which it can be an effective, patient-centered approach to treating individuals with diabetes. Methods: Semi-structured interviews were conducted with staff (n = 15) and patients (n = 18). Clinical data were extracted from the electronic medical record of patients (n = 1,599) seen at a safety-net health system in Chicago, Illinois, United States. The mixed methods study was guided by implementation science and participatory research principles. Staff interviews were 60 min and covered patient care activities, work flow, perceived patient experience, and facilitators/barriers to care coordination. Patient interviews were 60 min and covered satisfaction, attitudes about diabetes management, quality of life, and technology. Patient interviews were co-analyzed by research staff and members of a patient advisory committee. Clinical data were collected at an index visit, two years prior and at one-year follow up (n = 1,599). Results: Four themes emerged from the interviews: (1) patients perceived the TBC model to be patient centered and of high quality; (2) technology can be an innovative tool, but barriers exist; (3) diabetes management is a complex process; and (4) staff communication enhances care coordination, but misinterpreting roles reduces care coordination. From pre-enrollment to the follow-up period, we found a statistically significant increase in missed visits, decrease in hemoglobin A1c (HbA1c), decrease in body mass index, and decrease in the percent of patients with high blood pressure. We found that each medical visit during the follow-up period was associated with an HbA1c decrease of 0.26 points. Conclusions: A TBC model is a patient-centered approach to providing care to patients with complex health needs, such as diabetes, patients were satisfied with the care they were receiving, and the model was associated with an improvement in clinical outcomes. [ABSTRACT FROM AUTHOR]

AbstractDeep Learning (DL) methods have dramatically increased in popularity in recent years. While its initial success was demonstrated in the classification and manipulation of image data, there has been significant growth in the application of DL methods to problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of Variational Autoencoders (VAEs), a popular unsupervised DL architecture commonly utilized for dimension reduction, imputation, and learning latent representations of complex data. We propose a new VAE architecture, NIMIWAE, that is one of the first to flexibly account for both ignorable and non-ignorable patterns of missingness in input features at training time. Following training, samples can be drawn from the approximate posterior distribution of the missing data can be used for multiple imputation, facilitating downstream analyses on high dimensional incomplete datasets. We demonstrate through statistical simulation that our method outperforms existing approaches for unsupervised learning tasks and imputation accuracy. We conclude with a case study of an EHR dataset pertaining to 12,000 ICU patients containing a large number of diagnostic measurements and clinical outcomes, where many features are only partially observed. [ABSTRACT FROM AUTHOR]

PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS: We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning–/deep learning–based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS: The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION: We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics. Extracting systemic anticancer therapy and RECIST response information from clinical notes. [ABSTRACT FROM AUTHOR]

Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17–1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS. [ABSTRACT FROM AUTHOR]

Within a multistate clinical cohort, SARS-CoV-2 antiviral prescribing patterns were evaluated from April 2022–June 2023 among nonhospitalized patients with SARS-CoV-2 with risk factors for severe COVID-19. Among 3247 adults, only 31.9% were prescribed an antiviral agent (87.6% nirmatrelvir/ritonavir, 11.9% molnupiravir, 0.5% remdesivir), highlighting the need to identify and address treatment barriers. [ABSTRACT FROM AUTHOR]

Purpose This study aimed to compare the impact of an electronic health record (EHR)–integrated perpetual inventory management system (EIMS) versus a traditional inventory management system (TIMS) on inventory accuracy, visibility, and turnover in a large academic tertiary hospital. Methods The quasi-experimental study was conducted over 12 months (a 6-month preimplementation period and a 6-month postimplementation period, with an 11-month washout period) at Houston Methodist Hospital. The EIMS was implemented following the use of a TIMS. A total of 114 matched inventory items from both systems were identified and compared. The primary outcome was inventory accuracy, calculated as cycle count accuracy. The secondary outcomes were inventory visibility and monthly inventory turnover rate. Results Analysis demonstrated a 6.02% absolute increase in inventory accuracy (P < 0.001) with use of the EIMS versus the TIMS. After adjusting for inflation, there was an increase in the captured cost of goods sold from $4.16 million to $5.16 million. The monthly inventory value, adjusted for inflation in the prices of studied inventory items, increased from $2.05 million to $2.33 million. The monthly inventory turnover rate increased from 2.03 to 2.23 turns per month (P = 0.305) when comparing data for the pre- and postimplementation periods. Inventory visibility increased from 133 inventory items to 264 inventory items after EIMS implementation, indicating a 98% visibility increase compared to preimplementation levels. Conclusion This study found that implementing an EIMS significantly increased pharmacy inventory accuracy and inventory visibility, which are essential for optimizing patient care and pharmacy financial management. [ABSTRACT FROM AUTHOR]

Purpose To describe our experiences implementing and iterating CYP2C19 genotype–guided clopidogrel pharmacogenetic clinical decision support (CDS) tools over time in the setting of a large health system–wide, preemptive pharmacogenomics program. Summary Clopidogrel-treated patients who are genetically predicted cytochrome P450 isozyme 2C19 (CYP2C19) intermediate or poor metabolizers have an increased risk of atherothrombotic events, some of which can be life-threatening. The Clinical Pharmacogenetics Implementation Consortium provides guidance for the use of clopidogrel based on CYP2C19 genotype in patients with cardiovascular and cerebrovascular diseases. Our multidisciplinary team implemented an automated, interruptive alert that fires when clopidogrel is ordered or refilled for biobank participants with structured CYP2C19 intermediate or poor metabolizer genomic indicators in the electronic health record. The implementation began with a narrow cardiovascular indication and setting and was then scaled in 4 primary dimensions: (1) clinical indication; (2) availability across health-system locations; (3) care venue (e.g. inpatient vs outpatient); and (4) provider groups (eg, cardiology and neurology). We iterated our approach over time based on evolving clinical evidence and proactive strategies to optimize CDS maintenance and sustainability. A key facilitator of expansion was socialization of the broader pharmacogenomics initiative among our academic medical center community, accompanied by clinician acceptance of pharmacogenetic alerts in practice. Conclusion A multidisciplinary collaboration is recommended to facilitate the use of CYP2C19 genotype–guided antiplatelet therapy in patients with cardiovascular and cerebrovascular diseases. Evolving clopidogrel pharmacogenetic evidence necessitates thoughtful iteration of implementation efforts and strategies to optimize long-term maintenance and sustainability. [ABSTRACT FROM AUTHOR]

To evaluate the balance in patients with total knee arthroplasty (TKA) and knee osteoarthrosis (KOA), measured by postural stability computerized evaluation (PSCE), and to evaluate the effect of post-TKA patients' characteristics in their performance on PSCE. An observational cross-sectional study was conducted in two sets of patients: (A) patients with KOA and primary TKA surgery scheduled and (B) patients who underwent primary TKA >9 months. Sociodemographic, radiographic, clinical and PSCE parameters (using the Biodex Balance System) were assessed. Post-TKA patients placed more load on the replaced knee than the contralateral osteoarthritic knee (p = 0.027). They had less imbalance on the balance tests performed with the eyes open, on stable (p = 0.032), and unstable platforms (p = 0.022). These patients also showed better postural stability in monopodalic stance, both standing on the TKA (p = 0.010) and contralateral knee (p = 0.017). Age, weight, pain on the operated knee, extension deficit on the operated knee, and Berg Balance Scale scores on post-TKA patients were significantly associated with their performance on PSCE tests. PSCE can be useful to quantify the balance of post-TKA and KOA patients. Total knee arthroplasty is the definitive treatment for knee osteoarthrosis and it has several benefits, but its effect on balance is still unknown. Postural stability computerized evaluation is used to evaluate balance on vestibular diseases and it has been recently studied on musculoskeletal conditions. Postural stability computerized evaluation may be useful to evaluate patients' balance before and after total knee arthroplasty. [ABSTRACT FROM AUTHOR]

In a large academic medical center, processes for ordering and receiving biomarker testing to inform cancer treatment in non–small cell lung cancer varied by whether tests were conducted internally or externally. Lack of standardization in biomarker testing processes, despite the availability of electronic health record solutions, contributes to clinician burden and may increase risk of delays in receipt of testing results; therefore, efforts to streamline ordering processes may benefit health systems. [ABSTRACT FROM AUTHOR]

The identity management (IM) solutions are designed for facilitating and managing in digital field that identifies to perform the authentication operation which has been widely used for the real-world applications. The blockchain-based IM solutions are provided which allows the user to take the control of his or her identity. The existing blockchain-based IM models and based identified the research gap and other opportunities that will improve the present research work. Also, the information for owners who are trustworthy, traceable, secure ownership for the medical data that created seriousness in the security issues. The techniques and systems were centralized made difficult for sharing the medical data that have increased the single point of failure. The blockchain technology enabled the transparent, dependable, and auditable computing in various industries that used the decentralized network in the public ledger. The main objective is to exchange data in a safe and secure manner by using unalterable logs. To achieve and to improve the transparency, security and privacy, the verifiable digital signature with controlled blockchain enabled data records (DVS-CBDR) is introduced. Performance study of the proposed system has been assessed by a number of studies, examining throughput (170 tx/s), transaction response time (21 s), latency (5 s), memory usage (200 MB), and CPU utilization (98 Hz) which is better than patient-controlled blockchain enabled electronic health records for healthcare (PcBEHR). [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS. Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS−Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p <.001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p =.015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p =.020). We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS−Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted. [ABSTRACT FROM AUTHOR]

Background: Emulation of the "target trial" (TT), a hypothetical pragmatic randomized controlled trial (RCT), using observational data can be used to mitigate issues commonly encountered in comparative effectiveness research (CER) when randomized trials are not logistically, ethically, or financially feasible. However, cardiovascular (CV) health research has been slow to adopt TT emulation. Here, we demonstrate the design and analysis of a TT emulation using electronic health records to study the comparative effectiveness of the addition of a disease-modifying anti-rheumatic drug (DMARD) to a regimen of methotrexate on CV events among rheumatoid arthritis (RA) patients. Methods: We used data from an electronic medical records-based cohort of RA patients from Northwestern Medicine to emulate the TT. Follow-up began 3 months after initial prescription of MTX (2000–2020) and included all available follow-up through June 30, 2020. Weighted pooled logistic regression was used to estimate differences in CVD risk and survival. Cloning was used to handle immortal time bias and weights to improve baseline and time-varying covariate imbalance. Results: We identified 659 eligible people with RA with average follow-up of 46 months and 31 MACE events. The month 24 adjusted risk difference for MACE comparing initiation vs non-initiation of a DMARD was -1.47% (95% confidence interval [CI]: -4.74, 1.95%), and the marginal hazard ratio (HR) was 0.72 (95% CI: 0.71, 1.23). In analyses subject to immortal time bias, the HR was 0.62 (95% CI: 0.29–1.44). Conclusion: In this sample, we did not observe evidence of differences in risk of MACE, a finding that is compatible with previously published meta-analyses of RCTs. Thoughtful application of the TT framework provides opportunities to conduct CER in observational data. Benchmarking results of observational analyses to previously published RCTs can lend credibility to interpretation. [ABSTRACT FROM AUTHOR]

Background and aim: Renal dysfunction is associated with poor outcomes in patients with coronavirus disease 2019 (COVID-19). In an effort to improve outcomes, intravenous remdesivir has been broadly used for the treatment of COVID-19 even in patients with low estimated glomerular filtration rate (eGFR). Our study assessed the residual risk of outcomes of patients with low eGFR despite treatment with remdesivir for COVID-19, during a timeframe prior to the expanded label across all levels of renal function. Methods: We conducted an observational, retrospective, multi-site cohort study of adults hospitalized with COVID-19 treated with at least one dose of remdesivir between November 6, 2020, and November 5, 2021. Electronic medical records were reviewed to obtain patient characteristics, related laboratory data, and outcomes. The primary endpoint was all-cause mortality by day 28. Multivariable logistic regression was used to evaluate association between groups. Results: The study population consisted of 3024 patients hospitalized with COVID-19 and treated with remdesivir. The median age was 67 [IQR 55, 77] years; 42.7% were women, and 88.6% were white. The median eGFR was 76.6 mL/min/1.73 m2 [IQR 52.5, 95.2]; the majority (67.2%) of patients had an eGFR ≥ 60, while 9% had an eGFR <30. All-cause mortality by day 28 was 8.7%. All-cause mortality rates were significantly higher among patients with impaired renal function (Odds Ratio [OR] 1.63 for patients with eGFR 30–59; OR 1.46 for eGFR 15–29; OR 2.42 for eGFR <15 and OR 5.44 for patients on dialysis) compared to patients with eGFR ≥60 mL/min/1.73m2. Conclusions: Lower eGFR remains an independent risk factor for mortality in COVID-19 even in patients treated with remdesivir. [ABSTRACT FROM AUTHOR]

Stroke patients rarely have satisfactory survival, which worsens further if comorbidities develop in such patients. Limited data availability from Southeast Asian countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate predictors of in-hospital mortality in patients with ischemic stroke (IS). This retrospective observational study used IS medical records from the National Brain Centre Hospital, Jakarta, Indonesia. A theoretically driven Cox's regression and Fine-Gray models were established by controlling for age and sex to calculate the hazard ratio of each plausible risk factor for predicting in-hospital stroke mortality and addressing competing risks if they existed. This study finally included 3,278 patients with IS, 917 (28%) of whom had cardiovascular disease and 376 (11.5%) suffered renal disease. Bivariate exploratory analysis revealed lower blood levels of triglycerides, low density lipoprotein, and total cholesterol associated with in-hospital-stroke mortality. The average age of patients with post-stroke mortality was 64.06 ± 11.32 years, with a mean body mass index (BMI) of 23.77 kg/m2 and a median Glasgow Coma Scale (GCS) score of 12 and an IQR of 5. Cardiovascular disease was significantly associated with IS mortality risk. NIHSS score at admission (hazard ratio [HR] = 1.04; 95% confidence interval [CI]: 1.00–1.07), male sex (HR = 1.51[1.01–2.26] and uric acid level (HR = 1.02 [1.00–1.03]) predicted survivability. Comorbidities, such as cardiovascular disease (HR = 2.16 [1.37–3.40], pneumonia (HR = 2.43 [1.42–4.15] and sepsis (HR = 2.07 [1.09–3.94, had higher hazards for post-stroke mortality. Contrarily, the factors contributing to a lower hazard of mortality were BMI (HR = 0.94 [0.89–0.99]) and GCS (HReye = 0.66 [0.48–0.89]. In summary, our study reported that male sex, NIHSS, uric acid level, cardiovascular diseases, pneumonia, sepsis. BMI, and GCS on admission were strong determinants of in-hospital mortality in patients with IS. [ABSTRACT FROM AUTHOR]

PURPOSE: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM). METHODS: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method. RESULTS: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days. CONCLUSION: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research. This study derives structured lines of therapy for patients with glioblastoma from unstructured EHR data. [ABSTRACT FROM AUTHOR]

Background: As the opioid public health crisis evolves to include fentanyl and other potent synthetic opioids, more patients are admitted to the hospital with serious complications of drug use and frequently require higher levels of care, including intensive care unit (ICU) admission, for acute and chronic conditions related to opioid use disorder (OUD). This patient population poses a unique challenge when managing sedation and ensuring adequate ventilation while intubated given their high opioid requirements. Starting a patient on medications such as buprenorphine may be difficult for inpatient providers unfamiliar with its use, which may lead to undertreatment of patients with OUD, prolonged mechanical ventilation and length of stay. Methods: We developed a 7-day buprenorphine low dose overlap initiation (LDOI) schedule for patients with OUD admitted to the ICU (Table 1). Buprenorphine tablets were split by pharmacists and placed into pre-made blister packs as a kit to be loaded into the automated medication dispensing machine for nursing to administer daily. An internal quality review validated the appropriate dosing of split-dose tablets. To simplify order entry and increase prescriber comfort with this new protocol, we generated an order set within our electronic health record software with prebuilt buprenorphine titration orders. This protocol was implemented alongside patient and healthcare team education and counseling on the LDOI process, with follow-up offered to all patients upon discharge. Results: Here we report a series of 6 ICU patients started on buprenorphine using the LDOI schedule with split buprenorphine tablets. None of the 6 patients experienced precipitated withdrawal upon buprenorphine initiation using the LDOI schedule, and 5/6 patients were successfully extubated during the buprenorphine initiation. Four of six patients had a decrease in daily morphine milligram equivalents, with 3 patients transitioning to buprenorphine alone. Conclusion: Initiating buprenorphine via LDOI was found to be successful in the development of a protocol for critically ill patients with OUD. We examined LDOI of buprenorphine in intubated ICU patients and found no events of acute precipitated withdrawal. This protocol can be used as a guide for other institutions seeking to start critically ill patients on medication treatment for OUD during ICU admission. [ABSTRACT FROM AUTHOR]

The association between SARS-CoV-2 humoral immunity and post-acute sequelae of COVID-19 (long COVID) remains uncertain. The objective of this population-based cohort study was to assess the association between SARS-CoV-2 seropositivity and symptoms consistent with long COVID. English and Spanish-speaking members ≥ 18 years old with SARS-CoV-2 serologic testing conducted prior to August 2021 were recruited from Kaiser Permanente Southern California and Kaiser Permanente Colorado. Between November 2021 and April 2022, participants completed a survey assessing symptoms, physical health, mental health, and cognitive function consistent with long COVID. Survey results were linked to SARS-CoV-2 antibody (Ab) and viral (RNA) lab results in electronic health records. Weighted descriptive analyses were generated for five mutually exclusive patient groups: (1) +Ab/+RNA; (2) +Ab/- or missing RNA; (3) -Ab/+RNA; (4a) -Ab/-RNA reporting no prior infection; and (4b) -Ab/-RNA reporting prior infection. The proportions reporting symptoms between the +Ab/+RNA and -Ab/+RNA groups were compared, adjusted for covariates. Among 3,946 participants, the mean age was 52.1 years old (SD 15.6), 68.3% were female, 28.4% were Hispanic, and the serologic testing occurred a median of 15 months prior (IQR = 12–18). Three quarters (74.5%) reported having had COVID-19. Among people with laboratory-confirmed COVID-19, there was no association between antibody positivity (+Ab/+RNA vs. -Ab/+RNA) and any symptoms, physical health, mental health, or cognitive function. As expected, physical health, cognitive function, and fatigue were worse, and palpitations and headaches limiting the ability to work were more prevalent among people with laboratory-confirmed prior infection and positive serology (+Ab/+RNA) compared to those without reported or confirmed prior infection and negative serology (-Ab/-RNA/no reported COVID-19). Among people with laboratory-confirmed COVID-19, SARS-CoV-2 serology from practice settings were not associated with long COVID symptoms and health status suggesting limited utility of serology testing for long COVID. [ABSTRACT FROM AUTHOR]

Nigeria accounts for 39% of global malaria deaths in children under 5 years of age and the effective management of severe malaria is a health priority. The Annual Nigeria Severe Malaria Stakeholders Workshop, held on the 5–6th of July 2023 in Abuja, Nigeria brought together representatives from 36 States, the Federal Capital Territory, and other key stakeholders to address the management of severe malaria across all levels of the health service. Aims were to provide updates and review progress on severe malaria activities, the burden of disease, commodity logistics management, and pre-referral national policy implementation as well as to disseminate research findings. Two roundtable discussions were conducted to identify the challenges, barriers, and facilitators to the effective management of severe malaria in Nigeria. A key challenge was the limited awareness of updated guidelines and strategic documents among frontline health workers, leading to the misuse of non-recommended medications, like α-β-arteether. Further to this, the need to ensure appropriate treatments during pregnancy and the adoption of the WHO directive on the use of rectal artesunate were highlighted. To address these issues, innovative dissemination channels for guideline awareness were recommended and collaboration with professional organizations to enrich training materials emphasized. Other areas for improvement considered the processes involved in severe malaria management, with insufficient coordination among government agencies, inadequate referral linkages, and inadequate human resources identified as barriers. Recommendations focused on practical measures to minimize wastage of injectable artesunate, enhance data management through scaling up electronic medical records, and strengthen referral systems. The extension of severe malaria surveillance to patients older than 5 years was also proposed. To deliver these changes, actionable plans for sustained recruitment and training are needed, as well as committed advocacy at all levels to ensure timely fund disbursement and institutional support. A key overarching theme from the workshop was that a multifaceted approach was needed to address severe malaria in Nigeria, emphasizing collaborative efforts, evidence-based practices, and strategic resource allocation. With the largest malaria burden globally, the potential impact of addressing the challenges of severe malaria management in Nigeria cannot be understated and must be urgently addressed. [ABSTRACT FROM AUTHOR]

Using the electronic health record to address the underdiagnosis of Lynch syndrome [ABSTRACT FROM AUTHOR]

Introduction: Shared decision‐making (SDM) is a method of care by which patients and clinicians work together to co‐create a plan of care. Electronic health record (EHR) integration of SDM tools may increase adoption of SDM. We conducted a "lightweight" integration of a freely available electronic SDM tool, CV Prevention Choice, within the EHRs of three healthcare systems. Here, we report how the healthcare systems collaborated to achieve integration. Methods: This work was conducted as part of a stepped wedge randomized pragmatic trial. CV Prevention Choice was developed using guidelines for HTML5‐based web applications. Healthcare systems integrated the tool in their EHR using documentation the study team developed and refined with lessons learned after each system integrated the electronic SDM tool into their EHR. CV Prevention Choice integration populates the tool with individual patient data locally without sending protected health information between the EHR and the web. Data abstraction and secure transfer systems were developed to manage data collection to assess tool implementation and effectiveness outcomes. Results: Time to integrate CV Prevention Choice in the EHR was 12.1 weeks for the first system, 10.4 weeks for the second, and 9.7 weeks for the third. One system required two 1‐hour meetings with study team members and two healthcare systems required a single 1‐hour meeting. Healthcare system information technology teams collaborated by sharing information and offering improvements to documentation. Challenges included tracking CV Prevention Choice use for reporting and capture of combination medications. Data abstraction required refinements to address differences in how each healthcare system captured data elements. Conclusion: Targeted documentation on tool features and resource mapping supported collaboration of IT teams across healthcare systems, enabling them to integrate a web‐based SDM tool with little additional research team effort or oversight. Their collaboration helped overcome difficulties integrating the web application and address challenges to data harmonization for trial outcome analyses. [ABSTRACT FROM AUTHOR]

Introduction: Behavioral measurement‐based care (MBC) can improve patient outcomes and has also been advanced as a critical learning health system (LHS) tool for identifying and mitigating potential disparities in mental health treatment. However, little is known about the uptake of remote behavioral MBC in safety net settings, or possible disparities occurring in remote MBC implementation. Methods: This study uses electronic health record data to study variation in completion rates at the clinic and patient level of a remote MBC symptom measure tool during the first 6 months of implementation at three adult outpatient psychiatry clinics in a safety net health system. Provider‐reported barriers to MBC adoption were also measured using repeated surveys at one of the three sites. Results: Out of 1219 patients who were sent an MBC measure request, uptake of completing at least one measure varied by clinic: General Adult Clinic, 38% (n = 262 of 696); Substance Use Clinic, 28% (n = 73 of 265); and Transitions Clinic, 17% (n = 44 of 258). Compared with White patients, Black and Portuguese or Brazilian patients had lower uptake. Older patients also had lower uptake. Spanish language of care was associated with much lower uptake at the patient level. Significant patient‐level disparities in uptake persisted after adjusting for the clinic, mental health diagnoses, and number of measure requests sent. Providers cited time within visits and bandwidth in their workflow as the greatest consistent barriers to discussing MBC results with patients. Conclusions: There are significant disparities in MBC uptake at the patient and clinic level. From an LHS data infrastructure perspective, safety net health systems may need to address the need for possible ways to adapt MBC to better fit their populations and clinical needs, or identify targeted implementation strategies to close data gaps for the identified disparity populations. [ABSTRACT FROM AUTHOR]

Background: Virtual care increased dramatically during the COVID‐19 pandemic. The specific modality of virtual care (video, audio, eVisits, eConsults, and remote patient monitoring) has important implications for the accessibility and quality of care, but rates of use are relatively unknown. Methods for identifying virtual care modalities, especially in electronic health records (EHR) are inconsistent. This study (a) developed a method to identify virtual care modalities using EHR data and (b) described the distribution of these modalities over a 3‐year study period. Methods: EHR data from 316 primary care safety net clinics throughout the study period (4/1/2020‐3/31/2023) were included. Visit type (in‐person vs virtual) by adults >18 years old were classified. Expert consultation informed the development of two algorithms to classify virtual care visit modalities; these algorithms prioritized different EHR data elements. We conducted descriptive analyses comparing algorithms and the frequency of virtual care modalities. Results: Agreement between the algorithms was 96.5% for all visits and 89.3% for virtual care visits. The majority of disagreement between the algorithms was among encounters scheduled as audio‐only but billed as a video visit. Restricting to visits where the algorithms agreed on visit modality, there were 2‐fold more audio‐only than video visits. Conclusion: Visit modality classification varies depending upon which data in the EHR are prioritized. Regardless of which algorithm is utilized, safety net clinics rely on audio‐only and video visits to provide care in virtual visits. Elimination of reimbursement for audio visits may exacerbate existing inequities in care for low‐income patients. [ABSTRACT FROM AUTHOR]

Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. Methods: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. Results: The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. Conclusions: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes. [ABSTRACT FROM AUTHOR]

Background: The 24-h rooming-in policy is crucial to the Baby-Friendly Hospital Initiative (BFHI) for promoting breastfeeding. However, this policy may restrict maternal autonomy. In 2018, to integrate women's preferences into care decisions, Taiwan's Baby-Friendly certification included prenatal shared decision-making (SDM) for rooming-in. Prior to 2018, maternal knowledge, considerations, and intentions regarding rooming-in and the impact of prenatal SDM were unknown. Methods: A retrospective electronic medical record cohort study was conducted in southern Taiwan. Data on healthy postpartum women eligible for rooming-in and breastfeeding for the years 2017 and 2019, reflecting the periods before and after prenatal SDM was introduced, were gathered. Maternal and newborn characteristics, maternal knowledge, considerations, and prenatal intentions for postpartum rooming-in and breastfeeding during hospitalization were collected. Additionally, data on actual postpartum rooming-in practices during hospitalization and exclusive breastfeeding (EBF) practices from birth to hospital discharge, to 1 month, and to 2 months postpartum were collected. Descriptive and non-parametric statistics were applied to analyze the data. Results: A total of 621 women in 2017 and 311 women in 2019 were included. After prenatal SDM was introduced, the rooming-in rate during hospitalization fell from 42.2% in 2017 to 25.6% in 2019 (p < 0.001), and the EBF rate declined from 45.9% to 35.7% (p = 0.01). Additionally, the 1-month postpartum EBF rate decreased from 46.4% in 2017 (n = 571) to 44.3% in 2019 (n = 264), and the 2-month postpartum EBF rate dropped from 45.5% in 2017 (n = 591) to 40.2% (n = 308). According to the 2019 Patient Decision Aids responses (n = 236), women demonstrated limited understanding of rooming-in, with only 40.7% expressing an intention toward 24-h rooming-in. Women of older maternal age (p < 0.001), with a graduate degree (p = 0.02), full-time employment (p = 0.04), and concerns about rest disruption (p < 0.001), were more likely to prefer non-24-h rooming-in. Conclusions: Initiatives must promote prenatal SDM to enable healthcare providers to address misconceptions and tailor education, thereby increasing women's intention toward 24-h rooming-in and EBF. Future research should explore women's experiences and unmet needs at BFHI facilities to inform the construction of a baby- and mother-friendly environment. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to investigate how blood lipids are associated with diabetes among older Chinese adults. Methods: 3,268,928 older Chinese adults without known diabetes were included. Logistic regression and restricted cubic spline (RCS) models were conducted to study associations between blood lipids (total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], and high-density lipoprotein cholesterol [HDL-C]) and diabetes. Results: 202,832 diabetes cases were included. Compared with the lowest quintiles, TC, TG, and LDL-C in the highest quintiles showed a higher diabetes prevalence risk and HDL-C presented a lower risk in multivariate-adjusted logistic regression models. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for the highest quintiles of TC, TG, and HDL-C were 1.39 (1.37–1.41), 2.56 (2.52–2.60), and 0.73 (0.72–0.74), respectively. For LDL-C, 3–5% lower risk was found in the second and third quintiles, and 4–23% higher risk was found in the fourth and fifth quintiles. RCS curves showed a non-linear relationship between each blood lipid parameters and diabetes (P-non-linear < 0.001). TG and HDL-C curves presented monotonically increasing and L-shaped patterns, respectively, whereas TC and LDL-C curves exhibited a J-shaped pattern. When TC < 4.04 mmol/L or LDL-C < 2.33 mmol/L, ORs of diabetes increased with the decrease of corresponding indexes. However, after excluding participants with lower LDL-C, the J-shaped association with TC disappeared. Conclusions: This study demonstrates non-linear associations between lipids and diabetes. Low cholesterol levels are associated with a high risk of diabetes. The cholesterol paradox should be considered during lipid-lowering treatments. [ABSTRACT FROM AUTHOR]

Background: In 2017, a university-based academic healthcare system changed the opioid default pill count from 30 to 12 pills. Modifying the electronic default pill count influences short-term clinician prescribing practices. We sought to understand the long-term impact on postoperative opioid prescribing habits after an opioid default pill count reduction. Materials and methods: A retrospective electronic medical record system (EMRS) review was conducted in a healthcare system comprised of seven affiliated hospitals. Patients who underwent a surgical procedure and were prescribed an opioid on discharge between 2017–2021 were evaluated. All prescriptions were converted into morphine equivalents (MME). Analyses were performed with the chi-square test and Bonferonni adjusted t-test. Results: 191,379 surgical procedures were studied. The average quantity of opioids prescribed decreased from 32 oxycodone 5 mg tablets in 2017 to 21 oxycodone 5 mg tablets in 2021 (236 MME to 154 MME, p<0.001). The percentage of patients obtaining a refill within 90 days of surgery varied between 18.3% and 19.9% (p<0.001). Patients with a pre-existing opioid prescription and opioid-naïve patients both had significant reductions in prescription quantities above the default MME (79.7% to 60.6% vs. 65.3% to 36.9%, p<0.001). There was no significant change in refills for both groups (pre-existing 36.7% to 38.3% (p = 0.1) vs naïve 15.0% to 15.3% (p = 0.29)). Conclusions: The benefits of decreasing the default opioid pill count continue to accumulate long after the original change. Physician uptake of small changes to default EMRS practices represents a sustainable and effective intervention to reduce the quantities of postoperative opioids prescribed without deleterious effects on outpatient opiate requirements. [ABSTRACT FROM AUTHOR]

Purpose To assess the impact of a clinical pharmacy specialist (CPS) embedded within a rheumatology clinic at a large academic medical center on the prescription capture rate at the health-system specialty pharmacy. Methods Initially low prescription capture rates for the health-system specialty pharmacy led to the integration of a CPS in the main campus rheumatology clinic. Benchmarking was completed by assessing the prior prescription capture rate using electronic medical record analytics and Loopback Analytics (a database of prescription capture for the health-system specialty pharmacy). The existing workflows for both the rheumatology clinic and specialty pharmacy were observed with regard to biologic medication ordering and processing. Strategies for an updated workflow for biologic ordering with the incorporation of an embedded CPS in the rheumatology clinic were designed. This new workflow was established with key stakeholders, including the CPS, rheumatology providers, clinic staff, and pharmacy technicians. Once the workflow was established, all parties were educated and updated, including rheumatology providers, nursing staff, and specialty pharmacy staff. Prescription capture rate was monitored on a monthly basis. Results Prescription capture increased from 13.16% before pharmacist implementation (October to December 2021) to 35.42% after pharmacist implementation (October to December 2022) (P = 0.019). During the same periods, the revenue generated increased from $43,222.89 to $135,198.70 (P = 0.224) and the proportion of prescriptions initially sent to the health-system specialty pharmacy compared to other specialty pharmacies increased from 37% to 79% (P < 0.001) with CPS implementation. Conclusion Expansion and implementation of pharmacy services by integrating a CPS in a rheumatology ambulatory clinic increased prescription capture and pharmacy revenue while optimizing patient care. We hope to expand similar CPS services to other clinics within the health system. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The effectiveness of statin treatment to reduce coronary events and mortality has been hardly examined considering goals of LDL‐C. We aimed to analyse such association in secondary cardiovascular prevention.Retrospective cohort analysis of electronic health records from the SIDIAP database, Catalonia‐Spain. Recruitment period was from 2006 to 2017 and study period finished at the end of 2018. We included 54,175 people aged ≥35 years in cardiovascular secondary prevention starting statin treatment. We analysed the association of achieved LDL‐C goals after statin initiation with coronary heart disease and all‐cause mortality.Mean age was 69 years and 20,146 (37.2%) were women. Coronary heart disease occurred in 5687 (10.5%) participants, and 10,676 (19.7%) persons passed away. Median follow‐up lasted 5.7 years (interquartile range, 3.4–8.1). The coronary heart disease HRs (95% CI) for the LDL‐C goals of 70–100, <70–55 and <55 mg/dL were .86 (.81–.92), .83 (.76–.9) and .8 (.72–.88), respectively. They were .89 (.83–.96) in the group with 30%–40% reduction and .86 (.8–.93) in the groups with 40%–50% and ≥50% reduction. We observed no association with mortality. We observed no relevant differences by sex or age.This population‐level retrospective analysis of real‐world data observed that treatment with statins is effective to achieve certain LDL‐C goals and CHD reduction. The lack of significant difference between LDL‐C goals needs confirmation in additional studies with real‐world data. The LDL‐C target should consider the magnitude of the decrease in coronary events. [ABSTRACT FROM AUTHOR]

This article, titled "Unlocking The Spectrum: A Pictorial Journey through Nonatherosclerotic Lower Limb Claudication," aims to broaden clinicians' understanding of the various causes of lower limb claudication beyond atherosclerosis. The authors conducted a retrospective review of cases at their institution, analyzing clinical histories, physical examination findings, and diagnostic imaging modalities to identify diverse etiologies. The article includes a pictorial review of unusual causes of claudication, such as cystic adventitia of the popliteal artery and popliteal artery entrapment syndrome. The authors emphasize the importance of interdisciplinary collaboration in accurately diagnosing and managing this complex condition. [Extracted from the article]

Improving the overall care of children with medical complexity (CMC) is often beset by challenges in proactively identifying the population most in need of clinical management and quality improvement. The objective of the current study was to create a system to better capture longitudinal risk for sustained and elevated utilization across time using real-time electronic health record (EHR) data. A new Pediatric Population Management Classification (PPMC), drawn from visit diagnoses and continuity problem lists within the EHR of a tristate health system, was compared with an existing complex chronic conditions (CCC) system for agreement (with weighted κ) on identifying CCMC, as well as persistence of elevated charges and utilization from 2016 to 2019. Agreement of assignment PPMC was lower among primary care provider (PCP) populations than among other children traversing the health system for specialty or hospital services only (weighted κ 62% for PCP vs. 82% for non-PCP). The PPMC classification scheme, displaying greater precision in identifying CMC with persistently high utilization and charges for those who receive primary care within a large integrated health network, may offer a more pragmatic approach to selecting children with CMC for longitudinal care management. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is common, costly, and life-limiting, requiring dialysis and transplantation in advanced stages. Although effective guideline-based therapy exists, the asymptomatic nature of CKD together with low health literacy, adverse social determinants of health, unmet behavioral health needs, and primary care providers' (PCP) limited understanding of CKD result in defects in screening and diagnosis. Care is fragmented between PCPs and specialty nephrologists, with limited time, expertise, and resources to address systemic gaps. In this article, the authors define how they classified defects in care and report the current numbers of patients exposed to these defects, both nationally and in their health system Accountable Care Organization. They describe use of the health system's three-pillar leadership model (believing, belonging, and building) to empower providers to transform CKD care. Believing entailed engaging individuals to believe defects in CKD care could be eliminated and were a collective responsibility. Belonging fostered the creation of learning communities that broke down silos and encouraged open communication and collaboration between PCPs and nephrologists. Building involved constructing a fractal management infrastructure with transparent reporting and shared accountability, which would enable success in innovation and transformation. The result is proactive and relational CKD care organized around the patient's needs in University Hospitals Systems of Excellence. Systems of excellence combine multiple domains of expertise to promote best practice guidelines and integrate care throughout the system. The authors further describe a preliminary pilot of the CKD System of Excellence in primary care. [ABSTRACT FROM AUTHOR]

Introduction: Approximately, 5.5 million dental implants are estimated to be surgically placed in the United States yearly, with an anticipated long‐term failure rate ranging from 3% to 10%. At the Salt Lake City Dental Clinic within the Department of Veterans Affairs (VHA), specific protocols have been established to mandate that clinicians present every dental implant case for review by a committee. To understand the effectiveness of this approach, a comparative data analysis was undertaken to compare local dental implant failure data against national VHA data. Methods: Leveraging electronic health records of veterans spanning from 2000 to 2021, we gathered procedural records related to dental implant placement or failure, demographic information, and medical history for individuals who received dental care at various dental clinics within the nationwide VHA network. Subsequently, statistical analyses were conducted using mixed‐effects Poisson regression models with cluster‐robust standard errors. Incident rate ratios (IRRs) for Utah‐specific and nationwide cohorts were ascertained. Results: The Utah VHA dental clinical data showed that there was a slightly lower prevalence of implant failure at 6.7% compared to the national cohort, which had a rate of 6.9%. The implant level failure rates were also low, with 4.20 (confidence interval [CI]: 3.68, 4.81) per 1000 implant placements per year for Utah cohorts. The adjusted IRR indicated a relative 16% reduction in risk among Utah Veterans (IRR 0.84, 95% CI [0.76–0.92]; p < 0.001). Conclusions: The stringent protocols in place at Salt Lake City, which integrate evidence‐based practices and expert opinion for evaluating patient suitability for dental implant placement and subsequent care, contributed to the reduced risk among Utah Dental Clinic veterans pool compared to veterans of other states. [ABSTRACT FROM AUTHOR]

Background: Watch and wait (W&W) in complete clinical responders after neoadjuvant chemoradiotherapy has increasingly robust data supporting its oncological safety. Recently, studies have assessed the real‐world costs of this strategy compared to surgical resection. Our aim was to compare our oncological safety and costs associated with operative and surveillance strategies to international literature. Methods: Data were retrospectively collected and analysed via electronic health records from March 2014 to March 2021 in Christchurch, New Zealand. Two cohorts were created based on intention to treat. All hospital events were recorded and costed, as well as oncologic outcomes. Our primary endpoints were the cumulative cost of both strategies, 3‐year survival rate, and disease‐free survival. Results: Forty‐eight patients were identified who had rectal cancers resected (OT) with a yPT0N0 pathology, and 42 who were on the wait‐and‐watch (W&W) audit after having a clinical complete response. After exclusions, we identified 38 OT and 23 W&W patients; the W&W group were more co‐morbid (P = 0.05), had worse functional status (P = 0.008), higher BMI (P = 0.34) and more favourable clinical tumour staging (P = 0.01). The operative treatment (OT) group (n = 38) had more acute admissions (34% versus 13% in W&W, P = 0.08, OR 0.29). There was a 35.7% (n = 8 of 23) local recurrence in W&W and none in the OT group (P ≤ 0.001), with successful salvage in the W&W with local recurrence in 71.5% (n = 5 of 7). Three‐year distant metastasis‐free rate was 97.3% in the OT group and 90.9% in W&W (p = 0.05). Overall survival was 100% (W&W) and 94.7% (OT); (P = 0.019). Care in the OT group cost more than W&W, accounting for local regrowth management; $NZ70,759.56 versus $NZ47,905.52 (P = 0.014). Conclusion: This study found better oncological outcomes in the OT group, whilst the W&W group had reduced morbidity and acute bed days. The cost of wait and watch was approximately two‐thirds that of operative treatment, even accounting for salvage procedures for local regrowth. [ABSTRACT FROM AUTHOR]

This editorial comments on the article by Herasevich et al. [ABSTRACT FROM AUTHOR]

Background: Central nervous system (CNS) medication use is common among older adults, yet the impact of hospitalizations on use remains unclear. This study details CNS medication use, discontinuations, and user profiles during hospitalization periods. Methods: Retrospective cohort study using electronic health records on patients ≥65 years, from three hospitals (2018–2020), and prescribed a CNS medication around hospitalization (90 days prior to 90 days after). Latent class transitions analysis (LCTA) examined profiles of CNS medication class users across four time points (90 days prior, admission, discharge, 90 days after hospitalization). Results: Among 4666 patients (mean age 74.3 ± 9.3 years; 63% female; 70% White; mean length of stay 4.6 ± 5.6 days (median 3.0 [2.0, 6.0]), the most commonly prescribed CNS medications were antidepressants (56%) and opioids (49%). Overall, 74% (n = 3446) of patients were persistent users of a CNS medication across all four time points; 7% (n = 388) had discontinuations during hospitalization, but of these, 64% (216/388) had new starts or restarts within 90 days after hospitalization. LCTA identified three profile groups: (1) low CNS medication users, 54%–60% of patients; (2) mental health medication users, 30%–36%; and (3) acute/chronic pain medication users, 9%–10%. Probability of staying in same group across the four time points was high (0.88–1.00). Transitioning to the low CNS medication use group was highest from admission to discharge (probability of 9% for pain medication users, 5% for mental health medication users). Female gender increased (OR 2.4, 95% CI 1.3–4.3), while chronic kidney disease lowered (OR 0.5, 0.2–0.9) the odds of transitioning to the low CNS medication use profile between admission and discharge. Conclusions: CNS medication use stays consistent around hospitalization, with discontinuation more likely between admission and discharge, especially among pain medication users. Further research on patient outcomes is needed to understand the benefits and harms of hospital deprescribing, particularly for medications requiring gradual tapering. [ABSTRACT FROM AUTHOR]

Background: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR‐based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. Methods: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. Results: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83–0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98–12.87). Conclusions: This simple EHR‐based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well‐being of older adults at risk of critical illness and its consequences. See related article by Haimovich and Deardorff. [ABSTRACT FROM AUTHOR]

Background: Norovirus (NoV) can cause chronic relapsing and remitting diarrhea in immunocompromised patients. Few multicenter studies have described the clinical course, outcomes, and complications of chronic NoV in transplant recipients. Methods: A multicenter retrospective study of adult and pediatric SOT and HSCT recipients diagnosed with NoV between November 1, 2017, and February 28, 2021. Data were obtained from electronic medical records (EMR) and entered into a central REDCap database. Descriptive statistics were calculated. Results: A total of 280 NoV+ patients were identified across eight sites. The majority were adults (74.1%) and SOT recipients (91.4%). Initial diagnosis of NoV occurred a median of 36 months post‐Tx (IQR [15.0, 90.0]). Most NoV cases had >3 diarrheal episodes daily (66.0%), nausea and vomiting (60.1%). Duration of diarrhea varied greatly (median = 10 days, mean = 85.9 days, range (1, 2100)). 71.3% were hospitalized. Adjustment of immunosuppression, including reduction and discontinuation of mToR inhibitor, CNI, and/or MMF, was the most common management intervention for NoV. Other therapies resulted only in temporary improvement. Four patients died within 30 days and three others died by 180 days postdiagnosis. Clinically significant renal dysfunction was observed in 12.5% by 30 days and 21.4% by 180 days post‐NoV diagnosis. Conclusion: In HSCT and SOT patients, NoV frequently resulted in severe symptoms, prolonged diarrhea (30% persistent with diarrhea for >30 days), and clinically significant renal dysfunction (up to 21% of patients). Utilized therapies did not reliably result in the resolution of infection demonstrating the need for more effective treatment. [ABSTRACT FROM AUTHOR]

The harms that occur to patients in the hospital are relatively well documented. In contrast, less information is available about the harms that occur to outpatients, even though the amount of health care being delivered in outpatient settings and the complexity of that care are increasing. This article describes the frequency and types of harm that occurred at 11 outpatient sites in Massachusetts during 1 year. Visual Abstract. The Safety of Outpatient Health Care: The harms that occur to patients in the hospital are relatively well documented. In contrast, less information is available about the harms that occur to outpatients, even though the amount of health care being delivered in outpatient settings and the complexity of that care are increasing. This article describes the frequency and types of harm that occurred at 11 outpatient sites in Massachusetts during 1 year. Background: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. Objective: To measure AEs in the outpatient setting. Design: Retrospective review of the electronic health record (EHR). Setting: 11 outpatient sites in Massachusetts in 2018. Patients: 3103 patients who received outpatient care. Measurements: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. Results: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care–associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, −0.05 [CI, −0.09 to −0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. Limitation: Retrospective EHR review may miss AEs. Conclusion: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. Primary Funding Source: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions. [ABSTRACT FROM AUTHOR]

Background: Biologic drugs are highly effective for inflammatory bowel disease (IBD) management but are key drivers of costs of care especially when administered intravenously (i.v.). Availability of subcutaneous (SC) formulations has increased convenience for patients and improved access to care, but at the cost of revenue to health services. Aims: To evaluate the economic impact of transitioning a tertiary centre IBD cohort from i.v. to SC biologic administration and assess the implications for key stakeholders. Methods: A retrospective analysis of all patients who received i.v. infliximab or vedolizumab in the outpatient infusion centre of a tertiary IBD centre between July 2019 and June 2021 was undertaken. Data were collated from electronic medical records, pharmacy dispensing systems and the hospital business intelligence unit. An economic analysis and theoretical financial/capacity impact analysis of a transition to an SC model were estimated under two scenarios using a random 10% and 30% of the patient cohort. Results: Transitioning our IBD cohort from i.v. to SC administration would result in a loss to our health service of AU$2 732 123.75, composed of AU$1 463 003.75 in Weighted Inlier Equivalent Separation (WIES) and AU$1 269 120 in drug procurement revenue. However, it would ease capacity in the infusion centre by up to 5256 h. Conclusions: Transitioning patients to SC administration results in improved access to infusion centres and substantial savings to state governments; however, switching results in a loss of i.v. biologic‐generated WIES to health services. Alternative funding models are required to achieve sustainability in IBD care and reduce reliance on i.v. biologic‐generated income. [ABSTRACT FROM AUTHOR]

With increased access to biomedical and electronic health records data and the complexity of research questions, individuals in residency programmes who aim to conduct research require specialized educational programmes and biostatistics support. Biostatistics collaboration units in academic health centres often work with residents to conduct data‐intensive research. These units face numerous challenges related to providing training in statistical literacy and collaborating on resident‐led research within very restricted timelines. Since 2019, the Duke Biostatistics, Epidemiology, and Research Design (BERD) Methods Core has supported over 247 resident‐led projects by developing tools and resources to address these challenges. This manuscript presents novel processes and training materials that other institutions can use to help biostatistics collaboration units effectively support resident training programmes. We provide a framework to support the development of collaborative teams, along with specialized training materials for residents who collaborate with these teams. [ABSTRACT FROM AUTHOR]

Background: Retail health clinics offer easy access and lower costs in seeking nonemergent and usually focused care. The objective of this observational study was to describe the use of retail clinic services by women at MinuteClinic at CVS, the largest network of retail clinics in the United States. Methods: The retail clinic's large database included complete national data for every in-person encounter as recorded on the same electronic health record. Virtual care and pharmacist-delivered services like COVID-19 testing were excluded from the analysis. The primary reason for the visit and the patient's age group (<15, 15–44, 45–64, ≥65 years) and self-reported sex were recorded at each encounter from the most recent 5 years (January 1, 2018, to December 31, 2022). Results: There were 17,969,483 encounters by women seeking care, and women ≥15 years old were more likely than men to attend the clinics. Half of all encounters (50.6%) were for non-gynecologic acute care, whereas one-third (33.6%) dealt with either an infection or the need for a vaccination. Gynecologic reasons involved 5.6% of all encounters in women ≥15 years of age. No obstetrical care was provided except for pregnancy testing with referral, acute non-obstetric needs, or guideline-recommended vaccinations. Conclusion: Women, especially of reproductive age, are more inclined than men to seek care at retail clinics. Acute care is the most common need, although requests for immunizations, infection screening and treatment, and reproductive health issues occurred often. [ABSTRACT FROM AUTHOR]