Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations

BMJ Open

Volumn 7, Issue 9, 2017, Pages

  Hodgson, Luke Eliot ^a   Sarnowski, Alexander ^b   Roderick, Paul J ^a   Dimitrov, Borislav D ^a   Venn, Richard M ^c   Forni, Lui G ^b,d  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b ROYAL SURREY COUNTY HOSPITAL   (United Kingdom)

^c WORTHING HOSPITAL   (United Kingdom)

^d UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BICARBONATE; CREATININE; DIPEPTIDYL CARBOXYPEPTIDASE INHIBITOR;

ACUTE KIDNEY FAILURE; AUTOMATION; CALIBRATION; CHRONIC KIDNEY FAILURE; CREATININE BLOOD LEVEL; EMBASE; GENERAL HOSPITAL; HEART FAILURE; HUMAN; KIDNEY FUNCTION; MEDLINE; OUTCOME ASSESSMENT; PROGNOSTIC ASSESSMENT; QUALITY CONTROL; REVIEW; RISK ASSESSMENT; SAMPLE SIZE; SYSTEMATIC REVIEW; VALIDATION STUDY; WEB OF SCIENCE; BLOOD; HOSPITALIZATION; PROGNOSIS; RISK FACTOR; THEORETICAL MODEL;

ACUTE KIDNEY INJURY; CREATININE; HOSPITALIZATION; HOSPITALS, GENERAL; HUMANS; MODELS, THEORETICAL; PROGNOSIS; RISK ASSESSMENT; RISK FACTORS;

EID: 85030242565     PISSN: None     EISSN: 20446055     Source Type: Journal    
DOI: 10.1136/bmjopen-2017-016591     Document Type: Review

References (67)

1. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney Int 2012(Suppl 2): 1-136.
2. Susantitaphong P, Cruz DN, Cerda J, et al. World incidence of AKI: A meta-analysis. Clin J Am Soc Nephrol 2013; 8: 1482-93.
3. Bellomo R, Kellum JA, Ronco C. Acute kidney injury. Lancet 2012; 380: 756-66.
4. Bellomo R, Kellum J, Ronco C. Acute renal failure: Time for consensus. Intensive Care Med 2001; 27: 1685-8.
5. Bagshaw SM, Gibney RT. Conventional markers of kidney function. Crit Care Med 2008; 36(Suppl 4): S152-8.
6. National Confidential Enquiry into Patient Outcome and Death. Adding insult to injury: A review of the care of patients who died in hospital with a primary diagnosis of acute kidney injury (acute renal failure). 2009. NCEPOD http://www. ncepod. org. uk/2009report1/Downloads/AKI- report. pdf
7. National Institute for Health and Care Excellence. Acute Kidney Injury: Prevention, Detection and Management Up to the Point of Renal Replacement Therapy. (Clinical guideline CG169). London: National Clinical Guideline Centre, 2013.
8. Rabar S, Lau R, O'Flynn N, et al. Risk assessment of fragility fractures: Summary of NICE guidance. BMJ 2012; 345: E3698.
9. Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014; 129(25 Suppl 2): S49-73.
10. Mehta RL, Cerdá J, Burdmann EA, et al. International Society of Nephrology's 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): A human rights case for nephrology. Lancet 2015; 385: 2616-43.
11. Kyzas PA, Denaxa-Kyza D, Ioannidis JP. Almost all articles on cancer prognostic markers report statistically significant results. Eur J Cancer 2007; 43: 2559-79.
12. Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration. Ann Intern Med 2015; 162: W1-73.
13. Bouwmeester W, Zuithoff NP, Mallett S, et al. Reporting and methods in clinical prediction research: A systematic review. PLoS Med 2012; 9: E1001221-12.
14. Huen SC, Parikh CR. Predicting acute kidney injury after cardiac surgery: A systematic review. Ann Thorac Surg 2012; 93: 337-47.
15. Silver SA, Shah PM, Chertow GM, et al. Risk prediction models for contrast induced nephropathy: Systematic review. BMJ 2015; 351: H4395.
16. Caragata R, Wyssusek KH, Kruger P. Acute kidney injury following liver transplantation: A systematic review of published predictive models. Anaesth Intensive Care 2016; 44: 251-61.
17. Wilson T, Quan S, Cheema K, et al. Risk prediction models for acute kidney injury following major noncardiac surgery: Systematic review. Nephrol Dial Transplant 2016; 31: 231-40.
18. Kawamoto K, Houlihan CA, Balas EA, et al. Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ 2005; 330: 765.
19. Altman DG. Systematic reviews of evaluations of prognostic variables. BMJ 2001; 323: 224-8.
20. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med 2014; 11: E1001744.
21. Hospital Episode Statistics Analysis HaSCIC. Hospital Episode Statistics: Admitted patient care - 2014-15. 2015. Health and Social Care Information Centre http://www. hscic. gov. uk/pubs/hes1415
22. Selby NM, Crowley L, Fluck RJ, et al. Use of electronic results reporting to diagnose and monitor AKI in hospitalized patients. Clin J Am Soc Nephrol 2012; 7: 533-40.
23. Porter CJ, Juurlink I, Bisset LH, et al. A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital. Nephrol Dial Transplant 2014; 29: 1888-93.
24. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 2009; 6: E1000097.
25. Antman EM, Lau J, Kupelnick B, et al. A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. Treatments for myocardial infarction. JAMA 1992; 268: 240-8.
26. Oxman AD, Guyatt GH. The science of reviewing research. Ann N Y Acad Sci 1993; 703: 125-34.
27. Geersing GJ, Bouwmeester W, Zuithoff P, et al. Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One 2012; 7: E32844.
28. Wilczynski NL, McKibbon KA, Walter SD, et al. MEDLINE clinical queries are robust when searching in recent publishing years. J Am Med Inform Assoc 2013; 20: 363-8.
29. Vickers AJ, Elkin EB. Decision curve analysis: A novel method for evaluating prediction models. Med Decis Making 2006; 26: 565-74.
30. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115: 928-35.
31. Hosmer DW, Hjort NL. Goodness-of-fit processes for logistic regression: Simulation results. Stat Med 2002; 21: 2723-38.
32. Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited. Crit Care Med 2007; 35: 2052-6.
33. Kheterpal S, Tremper KK, Englesbe MJ, et al. Predictors of postoperative acute renal failure after noncardiac surgery in patients with previously normal renal function. Anesthesiology 2007; 107: 892-902.
34. Kheterpal S, Tremper KK, Heung M, et al. Development and validation of an acute kidney injury risk index for patients undergoing general surgery: Results from a national data set. Anesthesiology 2009; 110: 505-15.
35. Bell S, Dekker FW, Vadiveloo T, et al. Risk of postoperative acute kidney injury in patients undergoing orthopaedic surgery- development and validation of a risk score and effect of acute kidney injury on survival: Observational cohort study. BMJ 2015; 351: H5639.
36. Forni LG, Dawes T, Sinclair H, et al. Identifying the patient at risk of acute kidney injury: A predictive scoring system for the development of acute kidney injury in acute medical patients. Nephron Clin Pract 2013; 123: 143-50.
37. Matheny ME, Miller RA, Ikizler TA, et al. Development of inpatient risk stratification models of acute kidney injury for use in electronic health records. Med Decis Making 2010; 30: 639-50.
38. Drawz PE, Miller RT, Sehgal AR. Predicting hospital-acquired acute kidney injury-A case-controlled study. Ren Fail 2008; 30: 848-55.
39. Bedford M, Stevens P, Coulton S, et al. Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: A cohort and nested study. Southampton UK: Queen's Printer and Controller of HMSO, 2016.
40. Koyner JL, Adhikari R, Edelson DP, et al. Development of a Multicenter Ward-Based AKI Prediction Model. Clin J Am Soc Nephrol 2016; 11: 1935-43.
41. Breidthardt T, Socrates T, Noveanu M, et al. Effect and clinical prediction of worsening renal function in acute decompensated heart failure. Am J Cardiol 2011; 107: 730-5.
42. Wang YN, Cheng H, Yue T, et al. Derivation and validation of a prediction score for acute kidney injury in patients hospitalized with acute heart failure in a Chinese cohort. Nephrology 2013; 18: 489-96.
43. Forman DE, Butler J, Wang Y, et al. Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure. J Am Coll Cardiol 2004; 43: 61-7.
44. Xing XZ, Wang HJ, Huang CL, et al. Two acute kidney injury risk scores for critically ill cancer patients undergoing non-cardiac surgery. World J Emerg Med 2012; 3: 278-81.
45. Hodgson LE, Dimitrov BD, Roderick PJ, et al. Predicting AKI in emergency admissions: An external validation study of the acute kidney injury prediction score (APS). BMJ Open 2017; 7: E013511.
46. Noble D, Mathur R, Dent T, et al. Risk models and scores for type 2 diabetes: Systematic review. BMJ 2011; 343: D7163.
47. Collins GS, Mallett S, Omar O, et al. Developing risk prediction models for type 2 diabetes: A systematic review of methodology and reporting. BMC Med 2011; 9: 103.
48. Mallett S, Royston P, Dutton S, et al. Reporting methods in studies developing prognostic models in cancer: A review. BMC Med 2010; 8: 20.
49. Peduzzi P, Concato J, Feinstein AR, et al. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995; 48: 1503-10.
50. Donders AR, van der Heijden GJ, Stijnen T, et al. Review: A gentle introduction to imputation of missing values. J Clin Epidemiol 2006; 59: 1087-91.
51. Bihorac A, Brennan M, Ozrazgat-Baslanti T, et al. National surgical quality improvement program underestimates the risk associated with mild and moderate postoperative acute kidney injury. Crit Care Med 2013; 41: 2570-83.
52. Ensor J, Riley RD, Moore D, et al. Systematic review of prognostic models for recurrent venous thromboembolism (VTE) post-treatment of first unprovoked VTE. BMJ Open 2016; 6: E011190.
53. Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ 2017; 356: I6460.
54. Riley RD, Ensor J, Snell KI, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: Opportunities and challenges. BMJ 2016; 353: I3140.
55. Wonnacott A, Meran S, Amphlett B, et al. Epidemiology and outcomes in community-acquired versus hospital-acquired AKI. Clin J Am Soc Nephrol 2014; 9: 1007-14.
56. Xu X, Nie S, Liu Z, et al. Epidemiology and Clinical Correlates of AKI in Chinese Hospitalized Adults. Clin J Am Soc Nephrol 2015; 10: 1510-8.
57. Sawhney S, Fluck N, Fraser SD, et al. KDIGO-based acute kidney injury criteria operate differently in hospitals and the communityfindings from a large population cohort. Nephrol Dial Transplant 2016; 31: 922-9.
58. Kannry J, McCullagh L, Kushniruk A, et al; A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial. . Wash DC: EGEMS, 2015: 3. 1150.
59. Toll DB, Janssen KJ, Vergouwe Y, et al. Validation, updating and impact of clinical prediction rules: A review. J Clin Epidemiol 2008; 61: 1085-94.
60. Sinuff T, Adhikari NK, Cook DJ, et al. Mortality predictions in the intensive care unit: Comparing physicians with scoring systems. Crit Care Med 2006; 34: 878-85.
61. Brabrand M, Hallas J, Knudsen T. Nurses and physicians in a medical admission unit can accurately predict mortality of acutely admitted patients: A prospective cohort study. PLoS One 2014; 9: E101739.
62. Fillmore CL, Bray BE, Kawamoto K. Systematic review of clinical decision support interventions with potential for inpatient cost reduction. BMC Med Inform Decis Mak 2013; 13: 135.
63. Wilson FP, Shashaty M, Testani J, et al. Automated, electronic alerts for acute kidney injury: A single-blind, parallel-group, randomised controlled trial. Lancet 2015; 385: 1966-74.
64. Kolhe NV, Reilly T, Leung J, et al. A simple care bundle for use in acute kidney injury: A propensity score-matched cohort study. Nephrol Dial Transplant 2016; 31: 1846-54.
65. Kolhe NV, Staples D, Reilly T, et al. Impact of Compliance with a Care Bundle on Acute Kidney Injury Outcomes: A Prospective Observational Study. PLoS One 2015; 10: E0132279.
66. Murray PT, Mehta RL, Shaw A, et al. Potential use of biomarkers in acute kidney injury: Report and summary of recommendations from the 10th Acute Dialysis Quality Initiative consensus conference. Kidney Int 2014; 85: 513-21.
67. Koyner JL, Davison DL, Brasha-Mitchell E, et al. Furosemide Stress Test and Biomarkers for the Prediction of AKI Severity. J Am Soc Nephrol 2015; 26: 2023-31.