Improvement of drug prescribing in acute kidney injury with a nephrotoxic drug alert system

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

Exploring the Potential of Chatbots in Critical Care Nephrology

The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.

Kidney Disease Management in the Hospital Setting: A Focus on Inappropriate Drug Prescriptions in Older Patients

Aging with multimorbidity and polytherapy are the most significant factors that could led to inappropriate prescribing of contraindicated medications in patients with chronic kidney disease (CKD). The aim of this study was to evaluate the prescriptions of contraindicated drugs in older adults in CKD and to identify their associated factors in a hospital context. An observational retrospective study was carried out considering all patients ≥65 years with at least one serum creatinine value recorded into the REPOSI register into 2010-2016 period. The estimated glomerular filtration rate (eGFR) was applied to identify CKD. A descriptive analysis was performed to compare demographic and clinical characteristics; logistic regression models were used to estimate factors of inappropriate and percentage changes of drug use during hospitalization. A total of 4,713 hospitalized patients were recorded, of which 49.8% had an eGFR <60 ml/min/1.73 m²; the 21.9% were in treatment with at least one inappropriate drug at the time of hospital admission with a decrease of 3.0% at discharge (p = 0.010). The probability of using at least one contraindicated drug was significantly higher in patients treated with more several drugs (OR 1.21, 95% CI 1.16-1.25, p <0.001) and with CKD end-stages (G4: 16.90, 11.38-25.12, p < 0.001; G5: 19.38, 11.51-32.64, p < 0.001). Low-dose acetylsalicylic acid was the contraindicated drug mainly used at the time of admission, reducing 1.2% at discharge. An overall increase in therapeutic appropriateness in hospitalized older patients with CKD was observed, despite a small percentage of therapeutic inappropriateness at discharge that underlines the need for a closer collaboration with the pharmacologist to improve the drug management.

Effect of clinical decision support systems on clinical outcome for acute kidney injury: a systematic review and meta-analysis

Background

Clinical decision support systems including both electronic alerts and care bundles have been developed for hospitalized patients with acute kidney injury.

Methods

Electronic databases were searched for randomized, before-after and cohort studies that implemented a clinical decision support system for hospitalized patients with acute kidney injury between 1990 and 2019. The studies must describe their impact on care processes, patient-related outcomes, or hospital length of stay. The clinical decision support system included both electronic alerts and care bundles.

Results

We identified seven studies involving 32,846 participants. Clinical decision support system implementation significantly reduced mortality (OR 0.86; 95 % CI, 0.75–0.99; p = 0.040, I² = 65.3 %; n = 5 studies; N = 30,791 participants) and increased the proportion of acute kidney injury recognition (OR 3.12; 95 % CI, 2.37–4.10; p < 0.001, I² = 77.1 %; n = 2 studies; N = 25,121 participants), and investigations (OR 3.07; 95 % CI, 2.91–3.24; p < 0.001, I² = 0.0 %; n = 2 studies; N = 25,121 participants).

Conclusions

Nonrandomized controlled trials of clinical decision support systems for acute kidney injury have yielded evidence of improved patient-centered outcomes and care processes. This review is limited by the low number of randomized trials and the relatively short follow-up period.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02459-y.

Comparison of Prediction Models for Acute Kidney Injury Among Patients with Hepatobiliary Malignancies Based on XGBoost and LASSO-Logistic Algorithms

Background

Based on the admission data, we applied the XGBoost algorithm to create a prediction model to estimate the AKI risk in patients with hepatobiliary malignancies and then compare its prediction capacity with the logistic model.

Methods

We reviewed clinical data of 7968 and 589 liver/gallbladder cancer patients admitted to Zhongshan Hospital during 2014 and 2015. They were randomly divided into the training set and test set. Data were collected from the electronic medical record system. XGBoost and LASSO-logistic were used to develop prediction models, respectively. The performance measures included the classification matrix, the area under the receiver operating characteristic curve (AUC), lift chart and learning curve.

Results

Of 6846 participants in the training set, 792 (11.6%) cases developed AKI. In XGBoost model, the top 3 most important variables for AKI were serum creatinine (SCr), glomerular filtration rate (eGFR) and antitumor treatment in liver cancer patients. Similarly, SCr and eGFR also ranked second and third most important variables in the gallbladder cancer-related AKI model just after phosphorus. In the classification matrix, XGBoost model possessed a comparably better agreement between the actual observations and the predictions than LASSO-logistic model. The Youden’s index of XGBoost model was 47.5% and 59.3%, respectively, which was significantly higher than that of LASSO-logistic model (41.6% and 32.7%). The AUCs of XGBoost model were 0.822 in liver cancer and 0.850 in gallbladder cancer. By comparison, the AUC values of Logistic models were significantly lower as 0.793 and 0.740 (p=0.024 and 0.018). With the accumulation of training samples, XGBoost model maintained greater robustness in the learning curve.

Conclusion

XGBoost model based on admission data has higher accuracy and stronger robustness in predicting AKI. It will benefit AKI risk classification management in clinical practice and take an advanced intervention among patients with hepatobiliary malignancies.

Chronic Kidney Disease Management in General Practice: A Focus on Inappropriate Drugs Prescriptions

Nephrotoxic drugs prescriptions are often prescribed inappropriately by general practitioners (GPs), increasing the risk of chronic kidney disease (CKD). The aim of this study was to detect inappropriate prescriptions in patients with CKD and to identify their predictive factors. A retrospective study on patients with creatinine values recorded in the period 2014-2016 followed by 10 GPs was performed. The estimated glomerular filtration rate (eGFR) was used to identify CKD patients. The demographic and clinical characteristics and drugs prescriptions were collected. A descriptive analysis was conducted to compare the characteristics and logistic regression models to estimate the predictive factors of inappropriate prescriptions. Of 4098 patients with creatinine values recorded, 21.9% had an eGFR <60 mL/min/1.73m². Further, 56.8% received inappropriate prescriptions, with a significantly lower probability in subjects with at least a nephrologist visit (Adj OR 0.54 (95% CI 0.36-0.81)) and a greater probability in patients treated with more active substances (1.10 (1.08-1.12)), affected by more comorbidities (1.14 (1.06-1.230)), or with serious CKD (G4/G5 21.28 (7.36-61.57)). Nonsteroidal anti-inflammatory drugs (NSAIDs) were the most used contraindicated drugs (48.5%), while acetylsalicylic acid was the most inappropriately prescribed (39.5%). Our results highlight the inappropriate prescriptions for CKD authorized by GPs and underline the need of strategies to improve prescribing patterns.