Annals of Family Medicine最新文献

Purpose: Chest pain frequently poses a diagnostic challenge for general practitioners (GPs). Utilizing risk stratification tools might help GPs to rule out acute coronary syndrome (ACS) and make appropriate referral decisions. We conducted a systematic review of studies evaluating risk stratification tools for chest pain in primary care settings, both with and without troponin assays. Our aims were to assess the performance of tools for ruling out ACS and to provide a comprehensive review of the current evidence.

Methods: We searched PubMed and Embase for articles up to October 9, 2023 concerning adult patients with acute chest pain in primary care settings, for whom risk stratification tools (clinical decision rules [CDRs] and/or single biomarker tests) were used. To identify eligible studies, a combination of active learning and backward snowballing was applied. Screening, data extraction, and quality assessment (following the Quality Assessment of Diagnostic Accuracy Studies-2 tool) were performed independently by 2 researchers.

Results: Of the 1,204 studies screened, 14 were included in the final review. Nine studies validated 7 different CDRs without troponin. Sensitivities ranged from 75.0% to 97.0%, and negative predictive values (NPV) ranged from 82.4% to 99.7%. None of the CDRs outperformed the unaided judgment of GP's. Five studies reported on strategies using troponin measurements. Studies using high-sensitivity troponin showed highest diagnostic accuracy with sensitivity 83.3% to 100% and NPV 98.8% to 100%.

Conclusion: Clinical decision rules without troponin and the use of conventional troponin showed insufficient sensitivity to rule out ACS in primary care and are not recommended as standalone tools. High-sensitivity troponin strategies are promising, but studies are limited. Further prospective validation in primary care is needed before implementation.

Purpose: We undertook a study to investigate the relationship between duration of medication use and prevalence of impaired awareness of hypoglycemia (IAH) among patients with insulin-treated or sulfonylurea-treated type 2 diabetes in Taiwan.

Methods: A total of 898 patients (41.0% insulin users, 65.1% sulfonylurea users; mean [SD] age = 59.9 [12.3] years, 50.7% female) were enrolled in pharmacies, clinics, and health bureaus of Tainan City, Taiwan. Presence of IAH was determined with Chinese versions of the Gold questionnaire (Gold-TW) and Clarke questionnaire (Clarke-TW). Sociodemographics, disease and treatment histories, diabetes-related medical care, and health status were collected. We used multiple logistic regression models to assess the relationship between duration of medication use and IAH.

Results: Overall IAH prevalence was 41.0% (Gold-TW) and 28.2% (Clarke-TW) among insulin users, and 65.3% (Gold-TW) and 51.3% (Clarke-TW) among sulfonylurea users. Prevalence increased with the duration of sulfonylurea use, whereas it decreased with the duration of insulin use. After controlling for potential confounders, 5 or more years of sulfonylurea use was significantly associated with 3.50-fold (95% CI, 2.39-5.13) and 3.06-fold (95% CI, 2.11-4.44) increases in the odds of IAH based on the Gold-TW and Clarke-TW criteria, respectively. On the other hand, regular blood glucose testing and retinal examinations were associated with reduced odds in both insulin users and sulfonylurea users.

Conclusions: The prevalence of IAH was high among patients using sulfonylureas long term, but the odds of this complication were attenuated for those who received regular diabetes-related medical care. Our study suggests that long-term sulfonylurea use and irregular follow-up increase risk for IAH. Further prospective studies are needed to confirm the observed associations.Annals Early Access article.

Purpose: Relationship continuity of care has declined across English primary health care, with cross-sectional and longitudinal variations between general practices predicted by population and service factors. We aimed to describe cross-sectional and longitudinal variations across the COVID-19 pandemic and determine whether practice factors predicted the variations.

Methods: We conducted a longitudinal, ecological study of English general practices during 2018-2022 with continuity data, excluding practices with fewer than 750 patients or National Health Service (NHS) payments exceeding £500 per patient. Variables were derived from published data. The continuity measure was the product of weighted responses to 2 General Practice Patient Survey questions. In a multilevel mixed-effects model, the fixed effects were 11 variables' interactions with time: baseline continuity, NHS region, deprivation, location, percentage White ethnicity, list size, general practitioner and nurse numbers, contract type, NHS payments per patient, and percentage of patients seen on the same day as booking. The random effects were practices.

Results: Main analyses were based on 6,010 practices (out of 7,190 active practices). During 2018-2022, mean continuity in these practices declined (from 29.3% to 19.0%) and the coefficient of variation across practices increased (from 48.1% to 63.6%). Both slopes were steepest between 2021 and 2022. Practices having more general practitioners and higher percentages of patients seen the same day had slower declines. Practices having higher baseline continuity, located in certain non-London regions, and having higher percentages of White patients had faster declines. The remaining variables were not predictors.

Conclusions: Variables potentially associated with greater appointment availability predicted slower declines in continuity, with worsening declines and relative variability immediately after the COVID-19 lockdown, possibly reflecting surges in demand. To achieve better levels of continuity for those seeking it, practices can increase appointment availability within appointment systems that prioritize continuity.Annals Early Access article.

Purpose: Information about social determinants of health (SDOH) is essential for primary care clinicians in the delivery of equitable, comprehensive care, as well as for program planning and resource allocation. SDOH are rarely captured consistently in clinical settings, however. Artificial intelligence (AI) could potentially fill these data gaps, but it needs to be designed collaboratively and thoughtfully. We report on a codesign process with primary care clinicians to understand how an AI tool could be developed, implemented, and used in practice.

Methods: We conducted semistructured, 50-minute workshops with a large urban family health team in Toronto, Ontario, Canada asking their feedback on a proposed AI-based tool used to derive patient SDOH from electronic health record data. An inductive thematic analysis was used to describe participants' perspectives regarding the implementation and use of the proposed tool.

Results: Fifteen participants contributed across 4 workshops. Most patient SDOH information was not available or was difficult to find in their electronic health record. Discussions focused on 3 areas related to the implementation and use of an AI tool to derive social data: people, process, and technology. Participants recommended starting with 1 or 2 social determinants (income and housing were suggested as priorities) and emphasized the need for adequate resources, staff, and training materials. They noted many challenges, including how to discuss the use of AI with patients and how to confirm their social needs identified by the AI tool.

Conclusions: Our codesign experience provides guidance from end users on the appropriate and meaningful design and implementation of an AI-based tool for social data in primary care.

Purpose: The COVID-19 pandemic not only exacerbated existing disparities in health care in general but likely worsened disparities in access to primary care. Our objective was to quantify the nationwide decrease in primary care visits and increase in telehealth utilization during the pandemic and explore whether certain groups of patients were disproportionately affected.

Methods: We used a geographically diverse primary care electronic health record data set to examine the following 3 outcomes: (1) change in total visit volume, (2) change in in-person visit volume, and (3) the telehealth conversion ratio defined as the number of pandemic telehealth visits divided by the total number of prepandemic visits. We assessed whether these outcomes were associated with patient characteristics including age, gender, race, ethnicity, comorbidities, rurality, and area-level social deprivation.

Results: Our primary sample included 1,652,871 patients from 408 practices. During the pandemic we observed decreases of 7% and 17% in total and in-person visit volume and a 10% telehealth conversion ratio. The greatest decreases in visit volume were observed among pediatric patients (-24%), Asian patients (-11%), and those with more comorbidities (-9%). Telehealth usage was greatest among Hispanic or Latino patients (17%) and those living in urban areas (12%).

Conclusions: Decreases in primary care visit volume were partially offset by increasing telehealth use for all patients during the COVID-19 pandemic, but the magnitude of these changes varied significantly across all patient characteristics. These variations have implications not only for the long-term consequences of the COVID-19 pandemic, but also for planners seeking to ready the primary care delivery system for any future systematic disruptions.