Bellomo R, Kellum JA, Ronco C. “Acute kidney injury,” Lancet, vol. 380, no. 9843, pp. 756–766, Aug. 2012.

Wang HE, Muntner P, Chertow GM, Warnock DG. Acute kidney Injury and Mortality in Hospitalized Patients. Am J Nephrol. May 2012;35(4):349–55.

Case J, Khan S, Khalid R, Khan A. “Epidemiology of acute kidney injury in the intensive care unit,” Crit. Care Res. Pract, vol. 2013, 2013.

Hoste EAJ, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018;14(10):607–25.

CAS  PubMed  Google Scholar 

Bagshaw SM, George C, Bellomo R, Committee ADM. and others, “Early acute kidney injury and sepsis: a multicentre evaluation,” Crit. Care, vol. 12, no. 2, p. R47, 2008.

Kellum JA, et al. Kidney disease: improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2(1):1–138.

Google Scholar 

Sparrow HG, Swan JT, Moore LW, Gaber AO, Suki WN. Disparate outcomes observed within kidney disease: improving global outcomes (KDIGO) acute kidney injury stage 1. Kidney Int. 2019;95(4):905–13.

PubMed  Google Scholar 

Nateghi Haredasht F, Antonatou M, Cavalier E, Delanaye P, Pottel H, Makris K. “The effect of different consensus definitions on diagnosing acute kidney injury events and their association with in-hospital mortality,” J. Nephrol, vol. 35, no. 8, pp. 2087–2095, Nov. 2022.

Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40(2):221–6.

CAS  PubMed  Google Scholar 

Helmersson-Karlqvist J et al. “Cystatin C predicts long term mortality better than creatinine in a nationwide study of intensive care patients,” Sci. Reports 2021 111, vol. 11, no. 1, pp. 1–9, Mar. 2021.

Nateghi Haredasht F, Viaene L, Vens C, Callewaert N, De Corte W, Pottel H. “Comparison between Cystatin C- and Creatinine-Based Estimated Glomerular Filtration Rate in the Follow-Up of Patients Recovering from a Stage-3 AKI in ICU,” J. Clin. Med, vol. 11, no. 24, Dec. 2022.

Clerico A, Galli C, Fortunato A, Ronco C. “Neutrophil gelatinase-associated lipocalin (NGAL) as biomarker of acute kidney injury: A review of the laboratory characteristics and clinical evidences,” Clin. Chem. Lab. Med, vol. 50, no. 9, pp. 1505–1517, Sep. 2012.

Devarajan P. “NGAL in Acute Kidney Injury: From Serendipity to Utility,” Am. J. Kidney Dis, vol. 52, no. 3, pp. 395–399, Sep. 2008.

Soni SS et al. “NGAL: A biomarker of acute kidney injury and other systemic conditions,” Int. Urol. Nephrol, vol. 42, no. 1, pp. 141–150, Mar. 2010.

Srisawat N, et al. Urinary biomarkers and renal recovery in critically ill patients with renal support. Clin J Am Soc Nephrol. 2011;6(8):1815–23.

CAS  PubMed  PubMed Central  Google Scholar 

Murugan R, et al. Acute kidney injury in non-severe pneumonia is associated with an increased immune response and lower survival. Kidney Int. 2010;77(6):527–35.

CAS  PubMed  Google Scholar 

Sileanu FE, et al. AKI in low-risk versus high-risk patients in intensive care. Clin J Am Soc Nephrol. 2015;10(2):187–96.

PubMed  Google Scholar 

Network VARFT. Intensity of renal support in critically ill patients with acute kidney injury. N Engl J Med. 2008;359(1):7–20.

Google Scholar 

Beker BM, Corleto MG, Fieiras C, Musso CG. Novel acute kidney injury biomarkers: their characteristics, utility and concerns. Int Urol Nephrol. 2018;50(4):705–13.

CAS  PubMed  Google Scholar 

Hashemian SM, et al. Outcome of acute kidney injury in critical care unit, based on AKI network. Tanaffos. 2016;15(2):89.

PubMed  PubMed Central  Google Scholar 

Fuchs L, et al. Severity of acute kidney injury and two-year outcomes in critically ill patients. Chest. 2013;144(3):866–75.

PubMed  PubMed Central  Google Scholar 

Levey AS, et al. Chronic kidney disease as a global public health problem: approaches and initiatives–a position statement from kidney disease improving global outcomes. Kidney Int. 2007;72(3):247–59.

CAS  PubMed  Google Scholar 

Hsu RK, Hsu C. “The role of acute kidney injury in chronic kidney disease,” in Seminars in nephrology, 2016, vol. 36, no. 4, pp. 283–292.

Silver SA et al. “Ambulatory care after acute kidney injury: an opportunity to improve patient outcomes,” Can. J. kidney Heal. Dis, vol. 2, no. 1, Oct. 2015.

Chawla LS, Amdur RL, Amodeo S, Kimmel PL, Palant CE. The severity of acute kidney injury predicts progression to chronic kidney disease. Kidney Int. 2011;79(12):1361–9.

PubMed  PubMed Central  Google Scholar 

Silver SA et al. “Nephrologist follow-up versus usual care after an acute kidney injury hospitalization (Fusion): A randomized controlled trial,” Clin. J. Am. Soc. Nephrol, vol. 16, no. 7, pp. 1005–1014, Jul. 2021.

Kellum JA, Prowle JR. Paradigms of acute kidney injury in the intensive care setting. Nat Rev Nephrol. 2018;14(4):217.

PubMed  Google Scholar 

Boonstra A, Versluis A, Vos JFJ. Implementing electronic health records in hospitals: a systematic literature review. BMC Health Serv Res. 2014;14(1):370.

PubMed  PubMed Central  Google Scholar 

Panahiazar M, Taslimitehrani V, Pereira N, Pathak J. Using EHRs and machine learning for heart failure survival analysis. Stud Health Technol Inform. 2015;216:40.

PubMed  PubMed Central  Google Scholar 

Kawaler E, Cobian A, Peissig P, Cross D, Yale S, Craven M. “Learning to predict post-hospitalization VTE risk from EHR data,” in AMIA annual symposium proceedings, 2012, vol. 2012, p. 436.

Callahan A, Shah NH. “Machine learning in healthcare,” in Key Advances in Clinical Informatics,Elsevier, 2017,pp. 279–291.

Hilden J, Habbema JDF, Bjerregaard B. The measurement of performance in probabilistic diagnosis. Methods Inf Med. 1978;17(04):227–37.

CAS  PubMed  Google Scholar 

Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. ” Radiol. 1982;143(1):29–36.

CAS  Google Scholar 

Hosmer DW. “Assessing the fit of the model,”Appl. Logist. Regres., pp.143–202, 2000.

Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) the TRIPOD Statement. Circulation. 2015;131(2):211–9.

PubMed  PubMed Central  Google Scholar 

Hayden JA, Côté P, Bombardier C. “Evaluation of the quality of prognosis studies in systematic reviews,” Ann. Intern. Med, vol. 144, no. 6, pp. 427–437, Mar. 2006.

Wilson T, et al. Risk prediction models for acute kidney injury following major noncardiac surgery: systematic review. Nephrol Dial Transplant. 2016;31(2):231–40.

PubMed  Google Scholar 

Itenov TS, et al. Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model. Crit Care Resusc. 2018;20(1):54.

PubMed  Google Scholar 

James MT, et al. Derivation and external validation of prediction models for advanced chronic kidney disease following acute kidney injury. JAMA. 2017;318(18):1787–97.

PubMed  PubMed Central  Google Scholar 

Lee BJ, et al. Predicting renal recovery after dialysis-requiring acute kidney injury. Kidney Int reports. 2019;4(4):571–81.

Google Scholar 

Chen Z et al. “A novel predictive model for poor in-hospital outcomes in patients with acute kidney injury after cardiac surgery,” J. Thorac. Cardiovasc. Surg, May 2021.

He J, Lin J, Duan M. “Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury,” Front. Med, vol. 0, p. 2407, Dec. 2021.

Huang CY et al. “Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults,”J. Clin. Monit. Comput., 2022.

Pike F, et al. Biomarker enhanced risk prediction for adverse outcomes in critically ill patients receiving RRT. Clin J Am Soc Nephrol. 2015;10(8):1332–9.

PubMed  PubMed Central  Google Scholar 

Hobson CE, et al. Acute kidney injury is associated with increased long-term mortality after cardiothoracic surgery. Circulation. May 2009;119(18):2444–53.

Chawla LS, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 workgroup. Nat Rev Nephrol. Feb. 2017;2017 134(4):241–57.

Huang CY et al. “Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults,”J. Clin. Monit. Comput., pp.1–13, May 2022.

Huang C-Y et al. “Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults.&#8221

Sterne JAC et al. “Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls,” BMJ, vol. 338, no. 7713, pp. 157–160, Jul. 2009.

Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos V. “Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment,” Med. 2020, Vol. 56, Page 455, vol. 56, no. 9, p. 455, Sep. 2020.

Kennedy EH, Wiitala WL, Hayward RA, Sussman JB. Improved cardiovascular risk prediction using nonparametric regression and electronic health record data. Med Care. 2013;51(3):251.

PubMed  PubMed Central  Google Scholar 

Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.

Google Scholar 

Luo G. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Netw Model Anal Heal Informatics Bioinforma. Dec. 2016;5(1):1–16.

McDonald HI, Shaw C, Thomas SL, Mansfield KE, Tomlinson LA, Nitsch D. Methodological challenges when carrying out research on CKD and AKI using routine electronic health records. Kidney Int. 2016;90(5):943–9.

PubMed  Google Scholar 

Nateghi Haredasht F, Vens C. “Predicting Survival Outcomes in the Presence of Unlabeled Data,” Mach. Learn, Nov. 2022.