Clinical Decision Support System (CDSS) in primary care: from pragmatic use to the best approach to assess their benefit/risk profile in clinical practice

Temporal validation of a Generalized Additive2 Model (GA2M) to assess the risk of Chronic Kidney Disease (CKD)

OBJECTIVE

To assess the temporal validity of a model predicting the risk of Chronic Kidney Disease (CKD) using Generalized Additive2 Models (GA2M).

MATERIALS

We adopted the Italian Health Search Database (HSD) with which the original algorithm was developed and validated by comparing different machine learnings models.

METHODS

We selected all patients aged >=15 being active in HSD in 2019. They were followed up until December 2022 so being updated with three years of data collection. Those with prior diagnosis of CKD were excluded. A GA2M-based algorithm for CKD prediction was applied to this cohort in order to compare observed and predicted risk. Area Under Curve (AUC) and Average Precision (AP) were calculated.

RESULTS

We obtained an AUC and AP equal to 88% and 30%, respectively.

DISCUSSION

The prediction accuracy of the algorithm was largely consistent with that obtained in our prior work which was based on a different time-window for data collection. We therefore underlined and demonstrated the relevance of temporal validation for this prediction tool.

CONCLUSION

The GA2M confirmed its high accuracy in prediction of CKD. As such, the respective patient- and population-based informatic tools might be implemented in primary care.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

To support the use of NT-proBNP to better detect heart failure in patients with type 2 diabetes

PURPOSE

Heart failure (HF) is a chronic disease that causes approximately 300,000 and 250,000 deaths per year in Europe and United States, respectively. Type 2 Diabetes Mellitus (T2DM) is one the major risk factors of HF, and the investigation of NT-proBNP might support the early identification of HF in T2DM sufferers. Nevertheless, this parameter is poorly investigated. Thus, we aimed to demographically and clinically characterize diabetic patients which were prescribed with NT-proBNP in the primary care setting.

METHODS

Using a primary care database, we formed a cohort of patients aged ≥18 years diagnosed with T2DM between 2002 and 2021. A multivariate Cox model was adopted to assess the determinants associated with the prescription of NT-proBNP.

RESULTS

Among 167,961 T2DM patients, 7558 (4.5%, 95% CI: 4.4-4.6) were prescribed with NT-proBNP. Males and increasing age were expectedly associated with a higher propensity to be prescribed with NT-proBNP. In addition, a significant association was found for those suffering from obesity, ischemic cardiomyopathy, stroke, atrial fibrillation, hypertension, and with a Charlson Index of 2+.

CONCLUSION

These determinants might contribute to investigate the NT-proBNP in T2DM sufferers. A decision support system to appropriately ease the prescription of NT-proBNP might be therefore implemented in primary care settings.

Early identification of chronic kidney disease: it is time to enhance patient and population-based informatics tools for general practitioners

Chronic kidney disease (CKD) is a global public health issue that can lead to several complications such as, kidney failure, cerebro/cardiovascular disease, and death.There is a well-documented "awareness gap" among general practitioners (GPs) to recognize CKD. As shown by estimates stemming from the Health Search Database (HSD) of the Italian College of General Practitioners and Primary Care (SIMG), no substantial changes were observed in terms of the incident rate of CKD over the last 10 years. Namely, 10.3 to 9.5 per 1,000 new cases of CKD were estimated in 2012 and 2021, respectively. Thus, strategies to reduce under-recognized cases are needed. Early identification of CKD might improve patient's quality of life and clinical outcomes. In this context, patient- and population-based informatic tools may support both opportunistic and systematic screening of patients at greater risk of CKD. As such, the new effective pharmacotherapies for CKD would be proficiently administered. To this aim, these two complimentary tools have been developed and will be further implemented by GPs.The effectiveness of these instruments in identifying the condition at an early stage and reducing the burden of CKD on the national health system needs to be verified according to the new regulations on medical device (MDR: (EU) 2017/745).