Prediction of chronic kidney disease after acute kidney injury in ICU patients: study protocol for the PREDICT multicenter prospective observational study

Expectation of clinical decision support systems: a survey study among nephrologist end-users

BACKGROUND

Chronic kidney disease (CKD), a major public health problem with differing disease etiologies, leads to complications, comorbidities, polypharmacy, and mortality. Monitoring disease progression and personalized treatment efforts are crucial for long-term patient outcomes. Physicians need to integrate different data levels, e.g., clinical parameters, biomarkers, and drug information, with medical knowledge. Clinical decision support systems (CDSS) can tackle these issues and improve patient management. Knowledge about the awareness and implementation of CDSS in Germany within the field of nephrology is scarce.

PURPOSE

Nephrologists' attitude towards any CDSS and potential CDSS features of interest, like adverse event prediction algorithms, is important for a successful implementation. This survey investigates nephrologists' experiences with and expectations towards a useful CDSS for daily medical routine in the outpatient setting.

METHODS

The 38-item questionnaire survey was conducted either by telephone or as a do-it-yourself online interview amongst nephrologists across all of Germany. Answers were collected and analysed using the Electronic Data Capture System REDCap, as well as Stata SE 15.1, and Excel. The survey consisted of four modules: experiences with CDSS (M1), expectations towards a helpful CDSS (M2), evaluation of adverse event prediction algorithms (M3), and ethical aspects of CDSS (M4). Descriptive statistical analyses of all questions were conducted.

RESULTS

The study population comprised 54 physicians, with a response rate of about 80-100% per question. Most participants were aged between 51-60 years (45.1%), 64% were male, and most participants had been working in nephrology out-patient clinics for a median of 10.5 years. Overall, CDSS use was poor (81.2%), often due to lack of knowledge about existing CDSS. Most participants (79%) believed CDSS to be helpful in the management of CKD patients with a high willingness to try out a CDSS. Of all adverse event prediction algorithms, prediction of CKD progression (97.8%) and in-silico simulations of disease progression when changing, e. g., lifestyle or medication (97.7%) were rated most important. The spectrum of answers on ethical aspects of CDSS was diverse.

CONCLUSION

This survey provides insights into experience with and expectations of out-patient nephrologists on CDSS. Despite the current lack of knowledge on CDSS, the willingness to integrate CDSS into daily patient care, and the need for adverse event prediction algorithms was high.

Predicting outcomes of acute kidney injury in critically ill patients using machine learning

Acute Kidney Injury (AKI) is a sudden episode of kidney failure that is frequently seen in critically ill patients. AKI has been linked to chronic kidney disease (CKD) and mortality. We developed machine learning-based prediction models to predict outcomes following AKI stage 3 events in the intensive care unit. We conducted a prospective observational study that used the medical records of ICU patients diagnosed with AKI stage 3. A random forest algorithm was used to develop two models that can predict patients who will progress to CKD after three and six months of experiencing AKI stage 3. To predict mortality, two survival prediction models have been presented using random survival forests and survival XGBoost. We evaluated established CKD prediction models using AUCROC, and AUPR curves and compared them with the baseline logistic regression models. The mortality prediction models were evaluated with an external test set, and the C-indices were compared to baseline COXPH. We included 101 critically ill patients who experienced AKI stage 3. To increase the training set for the mortality prediction task, an unlabeled dataset has been added. The RF (AUPR: 0.895 and 0.848) and XGBoost (c-index: 0.8248) models have a better performance than the baseline models in predicting CKD and mortality, respectively Machine learning-based models can assist clinicians in making clinical decisions regarding critically ill patients with severe AKI who are likely to develop CKD following discharge. Additionally, we have shown better performance when unlabeled data are incorporated into the survival analysis task.

Outcome of acute kidney injury: how to make a difference?

Background

Acute kidney injury (AKI) is one of the most frequent organ failure encountered among intensive care unit patients. In addition to the well-known immediate complications (hydroelectrolytic disorders, hypervolemia, drug overdose), the occurrence of long-term complications and/or chronic comorbidities related to AKI has long been underestimated. The aim of this manuscript is to briefly review the short- and long-term consequences of AKI and discuss strategies likely to improve outcome of AKI.

Main body

We reviewed the literature, focusing on the consequences of AKI in all its aspects and the management of AKI. We addressed the importance of clinical management for improving outcomes AKI. Finally, we have also proposed candidate future strategies and management perspectives.

Conclusion

AKI must be considered as a systemic disease. Due to its short- and long-term impact, measures to prevent AKI and limit the consequences of AKI are expected to improve global outcomes of patients suffering from critical illnesses.

Care of the Survivor of Critical Illness and Acute Kidney Injury: A Multidisciplinary Approach

Acute kidney injury (AKI) is a common complication of critical illness and is associated with adverse short- and long-term health consequences. Survivors of critical illness and AKI experience poor kidney, cardiovascular and quality of life outcomes, along with increased mortality. Yet, many patients surviving AKI are unaware that there is a problem with their kidney health, and post-AKI nephrology follow-up occurs at very low rates. Although there is a paucity of evidence-based studies to guide post-AKI care, attention to risk factors such as hypertension and albuminuria are requisite. There are several ongoing or planned studies which are expected to help inform specific management in the future. Until then, a multidisciplinary approach is warranted to address areas such as quality of life, physical rehabilitation, dietary modifications, and medication reconciliation.

Nephrology and Public Policy Committee propositions to stimulate research collaboration in adults and children in Europe

The strengths and the limitations of research activities currently present in Europe are explored in order to outline how to proceed in the near future. Epidemiological and clinical research and public policy in Europe are generally considered to be comprehensive and successful, and the European Renal Association – European Dialysis and Transplant Association (ERA-EDTA) is playing a key role in the field of nephrology research. The Nephrology and Public Policy Committee (NPPC) aims to improve the current situation and translation into public policy by planning eight research topics to be supported in the coming 5 years by ERA-EDTA.