With the global population aging, the care of elderly cancer patients has become increasingly complex and significant. Comprehensive geriatric assessment (CGA), a multidimensional evaluation tool, has been widely implemented in oncology nursing to enhance the precision of treatment decisions and improve patient outcomes. This review examines the application of CGA in oncology nursing, drawing on literature published between 2010 and 2024 in major databases using keywords such as "Comprehensive Geriatric Assessment" and "Oncology Nursing". It highlights how CGA contributes to optimizing treatment selection, monitoring the treatment process, and improving patients' quality of life and long-term outcomes. CGA provides a comprehensive evaluation of elderly cancer patients, including physical, psychological, and social aspects, enabling the identification of high-risk patients and reducing treatment-related side effects and complications. It also offers a critical foundation for developing personalized care plans. The article discusses various practical examples of CGA implementation across different countries and regions, including multidisciplinary collaborative models in France, the United States, and Australia, demonstrating CGA's flexible application in diverse healthcare settings. Although significant progress has been made in applying CGA in oncology nursing, numerous challenges remain in its implementation, such as resource limitations and insufficient personnel training. Future research will focus on integrating CGA with emerging technologies, such as artificial intelligence and precision medicine, to further improve the quality of care and treatment outcomes for elderly cancer patients. By summarizing the current status and challenges of CGA in oncology nursing, this review provides guidance for future research and clinical practice, emphasizing the importance of advancing CGA application to meet the growing demands of elderly oncology care.

Myocardial injury is prevalent among patients hospitalized for COVID-19. However, the role of COVID-19 vaccines in modifying the risk of myocardial injury is unknown.

To assess the role of vaccines in modifying the risk of myocardial injury in COVID-19.

We enrolled COVID-19 patients admitted from March 2021 to February 2022 with known vaccination status and ≥1 assessment of hs-cTnI within 30 days from the admission. The primary endpoint was the occurrence of myocardial injury (hs-cTnI levels >99th percentile upper reference limit). A total of 1019 patients were included (mean age: 67.7 ± 14.8 years, 60.8% male, and 34.5% vaccinated against COVID-19). Myocardial injury occurred in 145 (14.2%) patients. At multivariate logistic regression analysis, advanced age, chronic kidney disease, and hypertension, but not vaccination status, were independent predictors of myocardial injury. In the analysis according to age tertiles distribution, myocardial injury occurred more frequently in the III tertile (≥76 years) compared with other tertiles (I tertile: ≤60 years; II tertile: 61-75 years) (P < 0.001). Moreover, in the III tertile, vaccination was protective against myocardial injury [odds ratio (OR): 0.57, 95% confidence interval (CI): 0.34-0.94; P = 0.03], while a previous history of coronary artery disease was an independent positive predictor. In contrast, in the I tertile, chronic kidney disease (OR: 6.94, 95% CI: 1.31-36.79, P = 0.02) and vaccination (OR: 4.44, 95% CI: 1.28-15.34, P = 0.02) were independent positive predictors of myocardial injury.

In patients ≥76 years, COVID-19 vaccines were protective for the occurrence of myocardial injury, while in patients ≤60 years, myocardial injury was associated with previous COVID-19 vaccination. Further studies are warranted to clarify the underlying mechanisms.

Real-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency.

In our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests.

The database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger.

Real-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.

COVID-19 pandemic has changed in-hospital care and was linked to superimposed infections. Here, we described epidemiology and risk factors for hospital-acquired bloodstream infections (HA-BSIs), before and during COVID-19 pandemic.

This retrospective, observational, single-center real-life study included 14,884 patients admitted to hospital wards and intensive care units (ICUs) with at least one blood culture, drawn 48 h after admission, either before (pre-COVID, N = 7382) or during pandemic (N = 7502, 1203 COVID-19+ and 6299 COVID-19-).

Two thousand two hundred and forty-five HA-BSI were microbiologically confirmed in 14,884 patients (15.1%), significantly higher among COVID-19+ (22.9%; ptrend < .001). COVID-19+ disclosed a significantly higher mortality rate (33.8%; p < .001) and more ICU admissions (29.7%; p < .001). Independent HAI-BSI predictors were: COVID-19 (OR: 1.43, 95%CI: 1.21-1.69; p < .001), hospitalization length (OR: 1.04, 95%CI: 1.03-1.04; p < .001), ICU admission (OR: 1.38, 95%CI: 1.19-1.60; p < .001), neoplasms (OR:1.48, 95%CI: 1.34-1.65; p < .001) and kidney failure (OR: 1.81, 95%CI: 1.61-2.04; p < .001). Of note, HA-BSI IRs for Acinetobacter spp. (0.16 × 100 patient-days) and Staphylococcus aureus (0.24 × 100 patient-days) peaked during the interval between first and second pandemic waves in our National context.

Patients with HA-BSI admitted before and during pandemic substantially differed. COVID-19 represented a risk factor for HA-BSI, though not confirmed in the sole pandemic period. Some etiologies emerged between pandemic waves, suggesting potential COVID-19 long-term effect on HA-BSIs.

Background Guidelines recommend using multiple drugs in patients with heart failure (HF) with reduced ejection fraction, but there is a paucity of real-world data on the simultaneous initiation of the 4 pharmacological pillars at discharge after a decompensation event. Methods and Results A retrospective data mart, including patients diagnosed with HF, was implemented. Consecutively admitted patients with HF with reduced ejection fraction were selected through an automated approach and categorized according to the number/type of treatments prescribed at discharge. The prevalence of contraindications and cautions for HF with reduced ejection fraction treatments was systematically assessed. Logistic regression models were fitted to assess predictors of the number of treatments (≥2 versus <2 drugs) prescribed and the risk of rehospitalization. A population of 305 patients with a first episode of HF hospitalization and a diagnosis of HF with reduced ejection fraction (ejection fraction, <40%) was selected. At discharge, 49.2% received 2 current recommended drugs, β-blockers were prescribed in 93.4%, while a renin-angiotensin system inhibitor or an angiotensin receptor-neprilysin inhibitor was prescribed in 68.2%. A mineralocorticoid receptor antagonist was prescribed in 32.5%, although none of the patients showed contraindications to mineralocorticoid receptor antagonist prescription. A sodium-glucose cotransporter 2 inhibitor could be prescribed in 71.1% of patients. On the basis of current recommendations, 46.2% could receive the 4 foundational drugs at discharge. Renal dysfunction was associated with <2 foundational drugs prescribed. After adjusting for age and renal function, use of ≥2 drugs was associated with lower risk of rehospitalization during the 30 days after discharge. Conclusions A quadruple therapy could be directly implementable at discharge, potentially providing prognostic advantages. Renal dysfunction was the main prevalent condition limiting this approach.

SARS-COV-2 pandemic led to antibiotic overprescription and unprecedented stress on healthcare systems worldwide. Knowing the comparative incident risk of bloodstream infection due to multidrug-resistant pathogens in COVID ordinary wards and intensive care-units may give insights into the impact of COVID-19 on antimicrobial resistance.

Single-center observational data extracted from a computerized dataset were used to identify all patients who underwent blood cultures from January 1, 2018 to May 15, 2021. Pathogen-specific incidence rates were compared according to the time of admission, patient's COVID status and ward type.

Among 14,884 patients for whom at least one blood culture was obtained, a total of 2534 were diagnosed with HA-BSI. Compared to both pre-pandemic and COVID-negative wards, HA-BSI due to S. aureus and Acinetobacter spp. (respectively 0.3 [95% CI 0.21-0.32] and 0.11 [0.08-0.16] new infections per 100 patient-days) showed significantly higher incidence rates, peaking in the COVID-ICU setting. Conversely, E. coli incident risk was 48% lower in COVID-positive vs COVID-negative settings (IRR 0.53 [0.34-0.77]). Among COVID + patients, 48% (n = 38/79) of S. aureus isolates were resistant to methicillin and 40% (n = 10/25) of K. pneumoniae isolates were resistant to carbapenems.

The data presented here indicate that the spectrum of pathogens causing BSI in ordinary wards and intensive care units varied during the pandemic, with the greatest shift experienced by COVID-ICUs. Antimicrobial resistance of selected high-priority bacteria was high in COVID positive settings.

Background: Cardiovascular sequelae after COVID-19 are frequent. However, the predictors for their occurrence are still unknown. In this study, we aimed to assess whether myocardial injury during COVID-19 hospitalization is associated to CV sequelae and death after hospital discharge. Methods: In this prospective observational study, consecutive patients who were admitted for COVID-19 in a metropolitan COVID-19 hub in Italy, between March 2021 and January 2022, with a ≥ 1 assessment of high sensitivity cardiac troponin I (hs-cTnI) were included in the study, if they were alive at hospital discharge. Myocardial injury was defined as elevation hs-cTnI > 99th percentile of the upper reference limit. The incidence of all-cause mortality and major adverse cardiovascular and cerebrovascular events (MACCE, including cardiovascular death, admission for acute or chronic coronary syndrome, hospitalization for heart failure, and stroke/transient ischemic attack) at follow-up were the primary outcomes. Arrhythmias, inflammatory heart diseases, and/or thrombotic disorders were analyzed as well. Results: Among the 701 COVID-19 survivors (mean age 66.4 ± 14.4 years, 40.2% female), myocardial injury occurred in 75 (10.7%) patients. At a median follow-up of 270 days (IQR 165, 380), all-cause mortality (21.3% vs. 6.1%, p < 0.001), MACCE (25.3% vs. 4.5%, p < 0.001), arrhythmias (9.3% vs. 5.0%, p = 0.034), and inflammatory heart disease (8.0% vs. 1.1%, p < 0.001) were more frequent in patients with myocardial injury compared to those without. At multivariate analysis, myocardial injury (HR 1.95 [95% CI:1.05−3.61]), age (HR 1.09 [95% CI:1.06−1.12]), and chronic kidney disease (HR 2.63 [95% CI:1.33−5.21]) were independent predictors of death. Myocardial injury (HR 3.92 [95% CI:2.07−7.42]), age (HR 1.05 [95% CI:1.02−1.08]), and diabetes (HR 2.35 [95% CI:1.25−4.43]) were independent predictors of MACCE. Conclusion: In COVID-19 survivors, myocardial injury during the hospital stay portends a higher risk of mortality and cardiovascular sequelae and could be considered for the risk stratification of COVID-19 sequelae in patients who are successfully discharged.