A dynamic model for predicting graft function in kidney recipients’ upcoming follow up visits: A clinical application of artificial neural network

Khazaee, Parviz Rashidi; Bagherzadeh, Jamshid; Niazkhani, Zahra; Pirnejad, Habibollah

doi:10.1016/j.ijmedinf.2018.09.012

Cited by 19 publications

(9 citation statements)

References 33 publications

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“…The outpatient transplant clinic of the Urmia Medical Science University (UMSU) has recently implemented a homegrown, renal transplant management system (RTMS). This system has been equipped with a computerized provider order entry (CPOE), a drug-drug interaction CDSS [ 28 ] and a glomerular filtration rate prediction CDSS for upcoming follow up visits [ 29 , 30 ]. Having such a system available in our clinic provided a unique opportunity to develop a DLI-CDSS, alongside others.…”

Section: Methodsmentioning

confidence: 99%

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Niazkhani

Fereidoni

Khazaee

et al. 2020

BMC Med Inform Decis Mak

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Background Drug-laboratory (lab) interactions (DLIs) are a common source of preventable medication errors. Clinical decision support systems (CDSSs) are promising tools to decrease such errors by improving prescription quality in terms of lab values. However, alert fatigue counteracts their impact. We aimed to develop a novel user-friendly, evidence-based, clinical context-aware CDSS to alert nephrologists about DLIs clinically important lab values in prescriptions of kidney recipients . Methods For the most frequently prescribed medications identified by a prospective cross-sectional study in a kidney transplant clinic, DLI-rules were extracted using main pharmacology references and clinical inputs from clinicians. A CDSS was then developed linking a computerized prescription system and lab records. The system performance was tested using data of both fictitious and real patients. The “Questionnaire for User Interface Satisfaction” was used to measure user satisfaction of the human-computer interface. Results Among 27 study medications, 17 needed adjustments regarding renal function, 15 required considerations based on hepatic function, 8 had drug-pregnancy interactions, and 13 required baselines or follow-up lab monitoring. Using IF & THEN rules and the contents of associated alert, a DLI-alerting CDSS was designed. To avoid alert fatigue, the alert appearance was considered as interruptive only when medications with serious risks were contraindicated or needed to be discontinued or adjusted. Other alerts appeared in a non-interruptive mode with visual clues on the prescription window for easy, intuitive notice. When the system was used for real 100 patients, it correctly detected 260 DLIs and displayed 249 monitoring, seven hepatic, four pregnancy, and none renal alerts. The system delivered patient-specific recommendations based on individual lab values in real-time. Clinicians were highly satisfied with the usability of the system. Conclusions To our knowledge, this is the first study of a comprehensive DLI-CDSS for kidney transplant care. By alerting on considerations in renal and hepatic dysfunctions, maternal and fetal toxicity, or required lab monitoring, this system can potentially improve medication safety in kidney recipients. Our experience provides a strong foundation for designing specialized systems to promote individualized transplant follow-up care.

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Section: Methodsmentioning

confidence: 99%

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Niazkhani

Fereidoni

Khazaee

et al. 2020

BMC Med Inform Decis Mak

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“…In a study conducted to predict the future values of estimated glomerular filtration rate (eGFR) for kidney recipients, Rashidi Khazaee at al [ 34 ] developed and validated an ANN-based model (multilayer perceptron network) using three static covariates of the recipients’ gender and the donors’ age and gender, as well as 11 dynamic covariates of the recipients including current age, time since transplant, serum creatinine, fasting blood sugar, weight, and blood pressures available at each visit. The development and validation datasets included 72.7% and 27.3% of the 25811 records from the historical visit data of 675 adult kidney recipients.…”

Section: Application Of Ai In Kidney Transplantationmentioning

confidence: 99%

Artificial intelligence and kidney transplantation

Seyahi¹,

Özcan²

2021

WJT

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Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

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“…This model could predict graft failure within the first year with a specificity of 80% [59]. Other stand-alone software solutions (trained on big data) incorporated pretransplant variables and predicted graft loss and mortality [60][61][62]. Since these AI tools can be easily integrated into electronic health records, we appreciate that all kidney transplants will be managed with AI tools in the next years.…”

Section: Key Messagesmentioning

confidence: 99%

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Burlacu

Iftene

Jugrin

et al. 2020

BioMed Research International

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Background. The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? Methods. We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. Results. AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. Conclusions. Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

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A dynamic model for predicting graft function in kidney recipients’ upcoming follow up visits: A clinical application of artificial neural network

Cited by 19 publications

References 33 publications

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system

Artificial intelligence and kidney transplantation

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

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