Epma Journal最新文献

Challenge in the framework of Predictive, Preventive and Personalised Medicine

In recent years, we have been witnessing a change in the performance of hospital pharmacists, aimed at increasing their participation in the pharmacotherapeutic process of patients. The drug cycle, characterised as multidisciplinary, is very complex. It is essential for the multidisciplinary team to have a broad vision of the medication system in order to guarantee safety and quality.

Considering the challenges of current healthcare systems and paradigm shift from reactive to predictive medicine, a new professional environment should be created to promote the implementation of Predictive, Preventive and Personalised Medicine in healthcare.

Objectives and study design

To optimise care times in multipurpose outpatient hospital both in the preparation of ready-to-use medications and in the dispensing of medications for home treatment.

To increase the confidence and value of hospital pharmacists in the process of patient and family care.

The design of the study was carried out by the following:

-Coordinating the schedules of the multi-pathology day hospital with the software and records of Medication Preparation in the pharmacy service.

-Opening a Pharmacy Outpatient Clinic associated with the multi-pathology day hospital.

-Planning and scheduling patient treatments.

Achievements

With the implementation of this programme, the visibility of hospital pharmacists in the multidisciplinary team was increased.

This Pharmacy Outpatient Clinic allowed the coordination of the pharmaceutical care process in the day hospital.

This project increased the credibility of the Pharmacy Service in the improvement of the integral process of the medicine.

Conclusions and expert recommendations

Predictive approach

The presence of pharmacists in the multi-pathology day hospital has a predictive approach. A change is made in the workflow that allows to generate a speed of intervention by acting before prescribing, dispensing and administering the treatment to the patient.

Targeted prevention

The presence of pharmacists in the multipurpose day hospital unit and their collaboration with other professionals and the patient bring about a selective prevention that decreases the possibility of medication errors occurring.

Personalisation of medical services

With the individualised dispensing of treatments, a step forward is taken in the personalisation of medical services, which avoids medication errors in labelling and administration and improves safety in the overall medication circuit in the hospital.

Background

Arterial stiffness is a major contributor to morbidity and mortality worldwide. Although several metabolic markers associated with arterial stiffness have been developed, there is limited data regarding whether glycemic control modifies the association between diabetes and arterial stiffness. For these reasons, identification of traits around diabetes will directly contribute to arterial stiffness and atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). Thus, this study aimed to explore the relationship of diabetes and glycemic control status with arterial stiffness in a real-world setting.

Methods

Data of participants from Beijing Xiaotangshan Examination Center (BXEC) with at least two surveys between 2008 and 2019 were used. Cumulative hazards were presented by inverse probability of treatment weighted (IPTW) Kaplan-Meier curves. Cox models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Arterial stiffness was defined as brachial-ankle pulse wave velocity (baPWV) ≥1400 cm/s.

Results

Of 5837 participants, the mean baseline age was 46.5±9.3 years, including 3791 (64.9%) males. During a median follow-up of 4.0 years, 1928 (33.0%) cases of incident arterial stiffness were observed. People with diabetes at baseline had a 48.4% (HR: 1.484, 95% CI: 1.250–1.761) excessive risk of arterial stiffness. Adherence to good glycemic control attenuated the relationship between diabetes and arterial stiffness (HR: 1.264, 95% CI: 0.950–1.681); while uncontrolled diabetes was associated with the highest risk of arterial stiffness (HR: 1.629, 95% CI: 1.323–2.005). Results were consistent using IPTW algorithm and multiple imputed data.

Conclusion

Our study quantified that diabetes status is closely associated with an increased risk of arterial stiffness and supported that adherence to good glycemic control could attenuate the adverse effect of diabetes on arterial stiffness. Therefore, glucose monitoring and control is a cost-effective strategy for the predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in early vascular damages and arterial stiffness.

Background

The human gut microbiota (GM) has been recognized as a significant factor in the development of insomnia, primarily through inflammatory pathways, making it a promising target for therapeutic interventions. Considering the principles of primary prediction, targeted prevention, and personalized treatment medicine (PPPM), identifying specific gut microbiota associated with insomnia and exploring the underlying mechanisms comprehensively are crucial steps towards achieving primary prediction, targeted prevention, and personalized treatment of insomnia.

Working hypothesis and methodology

We hypothesized that alterations in the composition of specific GM could induce insomnia through an inflammatory response, which postulates the existence of a GM-inflammation-insomnia pathway. Mendelian randomization (MR) analyses were employed to examine this pathway and explore the mediative effects of inflammation. We utilized genetic proxies representing GM, insomnia, and inflammatory indicators (including 41 circulating cytokines and C-reactive protein (CRP)), specifically identified from European ancestry. The primary method used to identify insomnia-related GM and examine the medicative effect of inflammation was the inverse variance weighted method, supplemented by the MR-Egger and weighted median methods. Our findings have the potential to identify individuals at risk of insomnia through screening for GM imbalances, leading to the development of targeted prevention and personalized treatment strategies for the condition.

Results

Nine genera and three circulating cytokines were identified to be associated with insomnia; only the associations of Clostridium (innocuum group) and β-NGF on insomnia remained significant after the FDR test, OR = 1.08 (95% CI = 1.04–1.12, P = 1.45 × 10⁻⁴, q = 0.02) and OR = 1.06 (95% CI = 1.02–1.10, P = 1.06 × 10⁻³, q = 0.04), respectively. CRP was associated with an increased risk of insomnia, OR = 1.05 (95% CI = 1.01–1.10, P = 6.42 × 10⁻³). CRP mediated the association of Coprococcus 1, Holdemania, and Rikenellaceae (RC9gut group) with insomnia. No heterogeneity or pleiotropy were detected.

Conclusions

Our study highlights the role of specific GM alterations in the development of insomnia and provides insights into the mediating effects of inflammation. Targeting these specific GM alterations presents a promising avenue for advancing the transition from reactive medicine to PPPM in managing insomnia, potentially leading to significant clinical benefits.

Background

Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs. Therefore, preselection of patients before ischemia testing needs to be improved using a more predictive and personalised approach.

Aims

Using multiple variables (symptoms, vitals, ECG, biomarkers), artificial intelligence–based tools can provide a detailed and individualised profile of each patient. This could improve PTP assessment and provide a more personalised diagnostic approach in the framework of predictive, preventive and personalised medicine (PPPM).

Methods

Consecutive patients (n = 2417) referred for Rubidium-82 positron emission tomography were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern–based algorithm (MPA) was applied incorporating symptoms, vitals, ECG and biomarkers. Five PTP categories from very low to very high PTP were defined (i.e., < 5%, 5–15%, 15–50%, 50–85%, > 85%). Ischemia was defined as summed difference score (SDS) ≥ 2.

Results

Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758, p < 0.001 compared to ESC 2013, 0.661; ESC 2019, 0.673; ACC 2012, 0.585; ACC 2021, 0.667). Using the < 5% threshold, the MPA’s sensitivity and negative predictive value to rule out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across PTP categories, reduced the proportion of patients in the intermediate (15–85%) range by 29% (ACC 2012)–51% (ESC 2019), and was the only tool to correctly predict ischemia prevalence in the very low PTP category.

Conclusion

The MPA model enhanced ischemia testing according to the PPPM framework:

1. 1)

   The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing (“predictive”).

2. 2)

   It reduced the proportion of patients in the intermediate PTP range. Therefore, it could be used as a gatekeeper to prevent patients from further unnecessary downstream testing, radiation exposure and costs (“preventive”).

3. 3)

   Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm (“personalised”).

Background: Intravenous leiomyomatosis (IVL) is a rare endocrine-associated tumor with unique characteristics of intravascular invasion. This study aimed to identify reliable biomarkers to supervise the development or recurrence of IVL in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods: A total of 60 cases were recruited to detect differentially expressed proteins (DEPs) in serum samples from IVL patients. These cases included those with recurrent IVL, non-recurrent IVL, uterine myoma, and healthy individuals without uterine myoma, with 15 cases in each category. Then, weighted gene co-expression network analysis (WGCNA), lasso-penalized Cox regression analysis (Lasso), trend clustering, and a generalized linear regression model (GLM) were utilized to screen the hub proteins involved in IVL progression.

Results: First, 93 differentially expressed proteins (DEPs) were determined from 2582 recognizable proteins, with 54 proteins augmented in the IVL group, and the remaining proteins declined. These proteins were enriched in the modulation of the immune environment, mainly by activating the function of B cells. After the integrated analyses mentioned above, a model based on four proteins (A0A5C2FUE5, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3) was developed to efficiently determine the potential of IVL lesions to progress. Among these featured proteins, our results demonstrated that the risk factor A0A5C2FUE5 was associated with IVL progression (OR = 2.64). Conversely, A0A5C2GPQ1, A0A5C2GNC7, and A0A5C2GBR3 might act in a protective manner and prevent disease development (OR = 0.32, 0.60, 0.53, respectively), which was further supported by the multi-class receiver operator characteristic curve analysis.

Conclusion: Four hub proteins were eventually identified based on the integrated bioinformatics analyses. This study potentiates the promising application of these novel biomarkers to predict the prognosis or progression of IVL by a 3PM approach.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00338-0.