#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Evaluating risk prediction models for adults with heart failure: A systematic literature review


Autoři: Gian Luca Di Tanna aff001;  Heidi Wirtz aff002;  Karen L. Burrows aff003;  Gary Globe aff002
Působiště autorů: Statistics Division, The George Institute for Global Health, Sydney, Australia aff001;  Global Health Economics, Amgen Inc., Thousand Oaks, CA, United States America aff002;  Curo Payer Evidence, Envision Pharma Group, Horsham, United Kingdom aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0224135

Souhrn

Background

The ability to predict risk allows healthcare providers to propose which patients might benefit most from certain therapies, and is relevant to payers’ demands to justify clinical and economic value. To understand the robustness of risk prediction models for heart failure (HF), we conducted a systematic literature review to (1) identify HF risk-prediction models, (2) assess statistical approach and extent of validation, (3) identify common variables, and (4) assess risk of bias (ROB).

Methods

Literature databases were searched from March 2013 to May 2018 to identify risk prediction models conducted in an out-of-hospital setting in adults with HF. Distinct risk prediction variables were ranked according to outcomes assessed and incorporation into the studies. ROB was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Results

Of 4720 non-duplicated citations, 40 risk-prediction publications were deemed relevant. Within the 40 publications, 58 models assessed 55 (co)primary outcomes, including all-cause mortality (n = 17), cardiovascular death (n = 9), HF hospitalizations (n = 15), and composite endpoints (n = 14). Few publications reported detail on handling missing data (n = 11; 28%). The discriminatory ability for predicting all-cause mortality, cardiovascular death, and composite endpoints was generally better than for HF hospitalization. 105 distinct predictor variables were identified. Predictors included in >5 publications were: N-terminal prohormone brain-natriuretic peptide, creatinine, blood urea nitrogen, systolic blood pressure, sodium, NYHA class, left ventricular ejection fraction, heart rate, and characteristics including male sex, diabetes, age, and BMI. Only 11/58 (19%) models had overall low ROB, based on our application of PROBAST. In total, 26/58 (45%) models discussed internal validation, and 14/58 (24%) external validation.

Conclusions

The majority of the 58 identified risk-prediction models for HF present particular concerns according to ROB assessment, mainly due to lack of validation and calibration. The potential utility of novel approaches such as machine learning tools is yet to be determined.

Registration number

The SLR was registered in Prospero (ID: CRD42018100709).

Klíčová slova:

Database searching – Ejection fraction – Forecasting – Health care providers – Health economics – Heart failure – Machine learning – Type 2 diabetes


Zdroje

1. Savarese G, Lund LH. Global Public Health Burden of Heart Failure. Card Fail Rev. 2017;3(1):7–11. Epub 2017/08/09. doi: 10.15420/cfr.2016:25:2 28785469; PubMed Central PMCID: PMC5494150.

2. Abid AR, Rafique S, Tarin SM, Ahmed RZ, Anjum AH. Age-related in-hospital mortality among patients with acute myocardial infarction. J Coll Physicians Surg Pak. 2004;14(5):262–6. Epub 2004/07/01. doi: 05.2004/JCPSP.262266 15225451.

3. Dunlay SM, Roger VL. Understanding the epidemic of heart failure: past, present, and future. Curr Heart Fail Rep. 2014;11(4):404–15. Epub 2014/09/04. doi: 10.1007/s11897-014-0220-x 25182014; PubMed Central PMCID: PMC4224604.

4. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2016;37(27):2129–200. Epub 2016/05/22. doi: 10.1093/eurheartj/ehw128 27206819.

5. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Colvin MM, et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2017;136(6):e137–e61. Epub 2017/04/30. doi: 10.1161/CIR.0000000000000509 28455343.

6. National Institute for Cardiovascular Outcomes Research (NICOR). National Heart Failure Audit April 2015—March 2016 Nov 9, 2018 Available from: http://www.ucl.ac.uk/nicor/audits/heartfailure/documents/annualreports/annual-report-2015-6-v8.pdf.

7. Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606–19. Epub 2013/04/26. doi: 10.1161/HHF.0b013e318291329a 23616602; PubMed Central PMCID: PMC3908895.

8. Lesyuk W, Kriza C, Kolominsky-Rabas P. Cost-of-illness studies in heart failure: a systematic review 2004–2016. BMC Cardiovasc Disord. 2018;18(1):74. Epub 2018/05/03. doi: 10.1186/s12872-018-0815-3 29716540; PubMed Central PMCID: PMC5930493.

9. McIlvennan CK, Eapen ZJ, Allen LA. Hospital readmissions reduction program. Circulation. 2015;131(20):1796–803. Epub 2015/05/20. doi: 10.1161/CIRCULATIONAHA.114.010270 25986448; PubMed Central PMCID: PMC4439931.

10. Howlett JG. Should we perform a heart failure risk score? Circ Heart Fail. 2013;6(1):4–5. Epub 2013/01/17. doi: 10.1161/CIRCHEARTFAILURE.112.973172 23322877.

11. Canepa M, Fonseca C, Chioncel O, Laroche C, Crespo-Leiro MG, Coats AJS, et al. Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry. JACC Heart Fail. 2018;6(6):452–62. Epub 2018/06/02. doi: 10.1016/j.jchf.2018.02.001 29852929.

12. Rahimi K, Bennett D, Conrad N, Williams TM, Basu J, Dwight J, et al. Risk prediction in patients with heart failure: a systematic review and analysis. JACC Heart Fail. 2014;2(5):440–6. Epub 2014/09/10. doi: 10.1016/j.jchf.2014.04.008 25194291.

13. Alba AC, Agoritsas T, Jankowski M, Courvoisier D, Walter SD, Guyatt GH, et al. Risk prediction models for mortality in ambulatory patients with heart failure: a systematic review. Circ Heart Fail. 2013;6(5):881–9. Epub 2013/07/28. doi: 10.1161/CIRCHEARTFAILURE.112.000043 23888045.

14. Ouwerkerk W, Voors AA, Zwinderman AH. Factors Influencing the Predictive Power of Models for Predicting Mortality and/or Heart Failure Hospitalization in Patients With Heart Failure. JACC Heart Fail. 2014;2(5):429–36. doi: 10.1016/j.jchf.2014.04.006 25194294

15. Wessler BS, Lai Yh L, Kramer W, Cangelosi M, Raman G, Lutz JS, et al. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database. Circ Cardiovasc Qual Outcomes. 2015;8(4):368–75. Epub 2015/07/15. doi: 10.1161/CIRCOUTCOMES.115.001693 26152680; PubMed Central PMCID: PMC4512876.

16. Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration. Ann Intern Med. 2019;170(1):W1–W33. Epub 2019/01/01. doi: 10.7326/M18-1377 30596876.

17. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med. 2019;170(1):51–8. Epub 2019/01/01. doi: 10.7326/M18-1376 30596875.

18. Moons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98(9):683–90. Epub 2012/03/09. doi: 10.1136/heartjnl-2011-301246 22397945.

19. National Institute for Health and Care Excellenc (NICE). Process and methods. London, UK2013. Available from: https://www.nice.org.uk/process/pmg9/resources/guide-to-the-methods-of-technology-appraisal-2013-pdf-2007975843781.

20. Hosmer DW, Lemeshow S. Applied Logistic Regression (2nd Edition). John Wiley & Sons. New York, NY 2000.

21. Adabag S, Rector TS, Anand IS, McMurray JJ, Zile M, Komajda M, et al. A prediction model for sudden cardiac death in patients with heart failure and preserved ejection fraction. Eur J Heart Fail. 2014;16(11):1175–82. Epub 2014/10/11. doi: 10.1002/ejhf.172 25302657.

22. Ahmad T, Fiuzat M, Neely B, Neely ML, Pencina MJ, Kraus WE, et al. Biomarkers of myocardial stress and fibrosis as predictors of mode of death in patients with chronic heart failure. JACC Heart Fail. 2014;2(3):260–8. Epub 2014/06/24. doi: 10.1016/j.jchf.2013.12.004 24952693; PubMed Central PMCID: PMC4224312.

23. Álvarez-García J, Ferrero-Gregori A, Puig T, Vázquez R, Delgado J, Pascual-Figal D, et al. A simple validated method for predicting the risk of hospitalization for worsening of heart failure in ambulatory patients: the Redin-SCORE. Eur J Heart Fail. 2015;17(8):818–27. Epub 2015/05/27. doi: 10.1002/ejhf.287 26011392; PubMed Central PMCID: PMC5032982.

24. Barlera S, Tavazzi L, Franzosi MG, Marchioli R, Raimondi E, Masson S, et al. Predictors of mortality in 6975 patients with chronic heart failure in the Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure trial: proposal for a nomogram. Circ Heart Fail. 2013;6(1):31–9. Epub 2012/11/16. doi: 10.1161/CIRCHEARTFAILURE.112.967828 23152490.

25. Behnes M, Bertsch T, Weiss C, Ahmad-Nejad P, Akin I, Fastner C, et al. Triple head-to-head comparison of fibrotic biomarkers galectin-3, osteopontin and gremlin-1 for long-term prognosis in suspected and proven acute heart failure patients. Int J Cardiol. 2016;203:398–406. Epub 2015/11/06. doi: 10.1016/j.ijcard.2015.10.127 26539964.

26. Betihavas V, Frost SA, Newton PJ, Macdonald P, Stewart S, Carrington MJ, et al. An Absolute Risk Prediction Model to Determine Unplanned Cardiovascular Readmissions for Adults with Chronic Heart Failure. Heart Lung Circ. 2015;24(11):1068–73. Epub 2015/06/07. doi: 10.1016/j.hlc.2015.04.168 26048319.

27. Bhandari SS, Narayan H, Jones DJ, Suzuki T, Struck J, Bergmann A, et al. Plasma growth hormone is a strong predictor of risk at 1 year in acute heart failure. Eur J Heart Fail. 2016;18(3):281–9. Epub 2015/12/17. doi: 10.1002/ejhf.459 26670643.

28. Bjurman C, Holmstrom A, Petzold M, Hammarsten O, Fu ML. Assessment of a multi-marker risk score for predicting cause-specific mortality at three years in older patients with heart failure and reduced ejection fraction. Cardiol J. 2015;22(1):31–6. Epub 2014/02/15. doi: 10.5603/CJ.a2014.0017 24526512.

29. Cabassi A, de Champlain J, Maggiore U, Parenti E, Coghi P, Vicini V, et al. Prealbumin improves death risk prediction of BNP-added Seattle Heart Failure Model: results from a pilot study in elderly chronic heart failure patients. Int J Cardiol. 2013;168(4):3334–9. Epub 2013/04/30. doi: 10.1016/j.ijcard.2013.04.039 23623341.

30. Carluccio E, Dini FL, Biagioli P, Lauciello R, Simioniuc A, Zuchi C, et al. The 'Echo Heart Failure Score': an echocardiographic risk prediction score of mortality in systolic heart failure. Eur J Heart Fail. 2013;15(8):868–76. Epub 2013/03/21. doi: 10.1093/eurjhf/hft038 23512095.

31. Carrasco-Sanchez FJ, Perez-Calvo JI, Morales-Rull JL, Galisteo-Almeda L, Paez-Rubio I, Baron-Franco B, et al. Heart failure mortality according to acute variations in N-terminal pro B-type natriuretic peptide and cystatin C levels. J Cardiovasc Med (Hagerstown). 2014;15(2):115–21. Epub 2014/02/14. doi: 10.2459/JCM.0b013e3283654bab 24522084.

32. Cubbon RM, Woolston A, Adams B, Gale CP, Gilthorpe MS, Baxter PD, et al. Prospective development and validation of a model to predict heart failure hospitalisation. Heart. 2014;100(12):923–9. Epub 2014/03/22. doi: 10.1136/heartjnl-2013-305294 24647052; PubMed Central PMCID: PMC4033182.

33. Demissei BG, Postmus D, Cleland JG, O'Connor CM, Metra M, Ponikowski P, et al. Plasma biomarkers to predict or rule out early post-discharge events after hospitalization for acute heart failure. Eur J Heart Fail. 2017;19(6):728–38. Epub 2017/03/03. doi: 10.1002/ejhf.766 28251755.

34. Demissei BG, Valente MA, Cleland JG, O'Connor CM, Metra M, Ponikowski P, et al. Optimizing clinical use of biomarkers in high-risk acute heart failure patients. Eur J Heart Fail. 2016;18(3):269–80. Epub 2015/12/05. doi: 10.1002/ejhf.443 26634889.

35. Eapen ZJ, Liang L, Fonarow GC, Heidenreich PA, Curtis LH, Peterson ED, et al. Validated, electronic health record deployable prediction models for assessing patient risk of 30-day rehospitalization and mortality in older heart failure patients. JACC Heart Fail. 2013;1(3):245–51. Epub 2014/03/14. doi: 10.1016/j.jchf.2013.01.008 24621877.

36. Fleming LM, Gavin M, Piatkowski G, Chang JD, Mukamal KJ. Derivation and validation of a 30-day heart failure readmission model. Am J Cardiol. 2014;114(9):1379–82. Epub 2014/09/10. doi: 10.1016/j.amjcard.2014.07.071 25200338.

37. Ford I, Robertson M, Komajda M, Bohm M, Borer JS, Tavazzi L, et al. Top ten risk factors for morbidity and mortality in patients with chronic systolic heart failure and elevated heart rate: The SHIFT Risk Model. Int J Cardiol. 2015;184:163–9. Epub 2015/02/24. doi: 10.1016/j.ijcard.2015.02.001 25703424.

38. Formiga F, Masip J, Chivite D, Corbella X. Applicability of the heart failure Readmission Risk score: A first European study. Int J Cardiol. 2017;236:304–9. Epub 2017/04/15. doi: 10.1016/j.ijcard.2017.02.024 28407978.

39. Freudenberger RS, Cheng B, Mann DL, Thompson JL, Sacco RL, Buchsbaum R, et al. The first prognostic model for stroke and death in patients with systolic heart failure. J Cardiol. 2016;68(2):100–3. Epub 2015/11/10. doi: 10.1016/j.jjcc.2015.09.014 26549533.

40. Frigola-Capell E, Comin-Colet J, Davins-Miralles J, Gich-Saladich I, Wensing M, Verdu-Rotellar JM. Trends and predictors of hospitalization, readmissions and length of stay in ambulatory patients with heart failure. Rev Clin Esp (Barc). 2013;213(1):1–7. Epub 2012/12/26. doi: 10.1016/j.rce.2012.10.006 23266127.

41. Hummel SL, Ghalib HH, Ratz D, Koelling TM. Risk stratification for death and all-cause hospitalization in heart failure clinic outpatients. Am Heart J. 2013;166(5):895–903 e1. Epub 2013/11/02. doi: 10.1016/j.ahj.2013.09.002 24176446; PubMed Central PMCID: PMC3896299.

42. Huynh QL, Negishi K, Blizzard L, Saito M, De Pasquale CG, Hare JL, et al. Mild cognitive impairment predicts death and readmission within 30days of discharge for heart failure. Int J Cardiol. 2016;221:212–7. Epub 2016/07/13. doi: 10.1016/j.ijcard.2016.07.074 27404677.

43. Jackson CE, Haig C, Welsh P, Dalzell JR, Tsorlalis IK, McConnachie A, et al. The incremental prognostic and clinical value of multiple novel biomarkers in heart failure. Eur J Heart Fail. 2016;18(12):1491–8. Epub 2016/04/27. doi: 10.1002/ejhf.543 27114189.

44. Jin M, Wei S, Gao R, Wang K, Xu X, Yao W, et al. Predictors of Long-Term Mortality in Patients With Acute Heart Failure. Int Heart J. 2017;58(3):409–15. Epub 2017/05/13. doi: 10.1536/ihj.16-219 28496020.

45. Keteyian SJ, Patel M, Kraus WE, Brawner CA, McConnell TR, Pina IL, et al. Variables Measured During Cardiopulmonary Exercise Testing as Predictors of Mortality in Chronic Systolic Heart Failure. J Am Coll Cardiol. 2016;67(7):780–9. Epub 2016/02/20. doi: 10.1016/j.jacc.2015.11.050 26892413; PubMed Central PMCID: PMC4761107.

46. Krumholz HM, Chaudhry SI, Spertus JA, Mattera JA, Hodshon B, Herrin J. Do Non-Clinical Factors Improve Prediction of Readmission Risk?: Results From the Tele-HF Study. JACC Heart Fail. 2016;4(1):12–20. Epub 2015/12/15. doi: 10.1016/j.jchf.2015.07.017 26656140; PubMed Central PMCID: PMC5459404.

47. Lassus J, Gayat E, Mueller C, Peacock WF, Spinar J, Harjola VP, et al. Incremental value of biomarkers to clinical variables for mortality prediction in acutely decompensated heart failure: the Multinational Observational Cohort on Acute Heart Failure (MOCA) study. Int J Cardiol. 2013;168(3):2186–94. Epub 2013/03/30. doi: 10.1016/j.ijcard.2013.01.228 23538053.

48. Lenzi J, Avaldi VM, Hernandez-Boussard T, Descovich C, Castaldini I, Urbinati S, et al. Risk-adjustment models for heart failure patients' 30-day mortality and readmission rates: the incremental value of clinical data abstracted from medical charts beyond hospital discharge record. BMC Health Serv Res. 2016;16:473. Epub 2016/09/08. doi: 10.1186/s12913-016-1731-9 27600617; PubMed Central PMCID: PMC5012069.

49. Leong KT, Wong LY, Aung KC, Macdonald M, Cao Y, Lee S, et al. Risk Stratification Model for 30-Day Heart Failure Readmission in a Multiethnic South East Asian Community. Am J Cardiol. 2017;119(9):1428–32. Epub 2017/03/18. doi: 10.1016/j.amjcard.2017.01.026 28302271.

50. Masson S, Batkai S, Beermann J, Bar C, Pfanne A, Thum S, et al. Circulating microRNA-132 levels improve risk prediction for heart failure hospitalization in patients with chronic heart failure. Eur J Heart Fail. 2018;20(1):78–85. Epub 2017/10/14. doi: 10.1002/ejhf.961 29027324.

51. Meijers WC, de Boer RA, van Veldhuisen DJ, Jaarsma T, Hillege HL, Maisel AS, et al. Biomarkers and low risk in heart failure. Data from COACH and TRIUMPH. Eur J Heart Fail. 2015;17(12):1271–82. Epub 2015/10/16. doi: 10.1002/ejhf.407 26466857.

52. Montero-Perez-Barquero M, Manzano L, Formiga F, Roughton M, Coats A, Rodriguez-Artalejo F, et al. Utility of the SENIORS elderly heart failure risk model applied to the RICA registry of acute heart failure. Int J Cardiol. 2015;182:449–53. Epub 2015/01/21. doi: 10.1016/j.ijcard.2014.12.173 25602297.

53. Nymo SH, Aukrust P, Kjekshus J, McMurray JJ, Cleland JG, Wikstrand J, et al. Limited Added Value of Circulating Inflammatory Biomarkers in Chronic Heart Failure. JACC Heart Fail. 2017;5(4):256–64. Epub 2017/04/01. doi: 10.1016/j.jchf.2017.01.008 28359413.

54. Ramirez J, Orini M, Minchole A, Monasterio V, Cygankiewicz I, Bayes de Luna A, et al. Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model. PLoS One. 2017;12(10):e0186152. Epub 2017/10/12. doi: 10.1371/journal.pone.0186152 29020031; PubMed Central PMCID: PMC5636125.

55. Shameer K, Johnson KW, Yahi A, Miotto R, Li LI, Ricks D, et al. Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort. Pac Symp Biocomput. 2016;Pacific Symposium on Biocomputing. 22:276–87. 617085391.

56. Sudhakar S, Zhang W, Kuo YF, Alghrouz M, Barbajelata A, Sharma G. Validation of the Readmission Risk Score in Heart Failure Patients at a Tertiary Hospital. J Card Fail. 2015;21(11):885–91. Epub 2015/07/26. doi: 10.1016/j.cardfail.2015.07.010 26209002.

57. Upshaw JN, Konstam MA, Klaveren D, Noubary F, Huggins GS, Kent DM. Multistate Model to Predict Heart Failure Hospitalizations and All-Cause Mortality in Outpatients With Heart Failure With Reduced Ejection Fraction: Model Derivation and External Validation. Circ Heart Fail. 2016;9(8). Epub 2016/08/16. doi: 10.1161/CIRCHEARTFAILURE.116.003146 27514751; PubMed Central PMCID: PMC5328587.

58. Uszko-Lencer N, Frankenstein L, Spruit MA, Maeder MT, Gutmann M, Muzzarelli S, et al. Predicting hospitalization and mortality in patients with heart failure: The BARDICHE-index. Int J Cardiol. 2017;227:901–7. Epub 2016/12/05. doi: 10.1016/j.ijcard.2016.11.122 27915084.

59. Vader JM, LaRue SJ, Stevens SR, Mentz RJ, DeVore AD, Lala A, et al. Timing and Causes of Readmission After Acute Heart Failure Hospitalization-Insights From the Heart Failure Network Trials. J Card Fail. 2016;22(11):875–83. doi: 10.1016/j.cardfail.2016.04.014 27133201.

60. Zai AH, Ronquillo JG, Nieves R, Chueh HC, Kvedar JC, Jethwani K. Assessing hospital readmission risk factors in heart failure patients enrolled in a telemonitoring program. Int J Telemed Appl. 2013;2013:305819. Epub 2013/05/28. doi: 10.1155/2013/305819 23710170; PubMed Central PMCID: PMC3655587.

61. Simpson J, McMurray JJV. Prognostic Modeling in Heart Failure: Time for a Reboot. JACC Heart Fail. 2018;6(6):463–4. Epub 2018/06/02. doi: 10.1016/j.jchf.2018.03.020 29852930.

62. Betts MB, Milev S, Hoog M, Jung H, Milenković D, Qian Y, et al. Comparison of Recommendations and Use of Cardiovascular Risk Equations by Health Technology Assessment Agencies and Clinical Guidelines. Value Health. 2019;22(2):210–9. doi: 10.1016/j.jval.2018.08.003 30711066

63. Baio G, Dawid AP. Probabilistic sensitivity analysis in health economics. Stat Methods Med Res. 2015;24(6):615–34. Epub 2011/09/21. doi: 10.1177/0962280211419832 21930515.

64. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944. Epub 2017/04/05. doi: 10.1371/journal.pone.0174944 28376093; PubMed Central PMCID: PMC5380334.

65. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22. Epub 2019/02/15. doi: 10.1016/j.jclinepi.2019.02.004 30763612.

66. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925–31. Epub 2014/06/06. doi: 10.1093/eurheartj/ehu207 24898551; PubMed Central PMCID: PMC4155437.

67. Cook NR. Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction. Circulation. 2007;115(7):928–35. doi: 10.1161/CIRCULATIONAHA.106.672402 17309939

68. Pencina MJ, D'Agostino RB Sr. Evaluating Discrimination of Risk Prediction Models: The C Statistic. JAMA. 2015;314(10):1063–4. Epub 2015/09/09. doi: 10.1001/jama.2015.11082 26348755.

69. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–38. Epub 2009/12/17. doi: 10.1097/EDE.0b013e3181c30fb2 20010215; PubMed Central PMCID: PMC3575184.

70. Felker GM, Anstrom KJ, Adams KF, Ezekowitz JA, Fiuzat M, Houston-Miller N, et al. Effect of Natriuretic Peptide-Guided Therapy on Hospitalization or Cardiovascular Mortality in High-Risk Patients With Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial. JAMA. 2017;318(8):713–20. Epub 2017/08/23. doi: 10.1001/jama.2017.10565 28829876; PubMed Central PMCID: PMC5605776.

71. Cacciatore F, Abete P, Mazzella F, Furgi G, Nicolino A, Longobardi G, et al. Six-minute walking test but not ejection fraction predicts mortality in elderly patients undergoing cardiac rehabilitation following coronary artery bypass grafting. Eur J Prev Cardiol. 2012;19(6):1401–9. Epub 2011/09/22. doi: 10.1177/1741826711422991 21933832.

72. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489–95. Epub 2005/09/01. doi: 10.1503/cmaj.050051 16129869; PubMed Central PMCID: PMC1188185.

73. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–56. Epub 2001/03/17. doi: 10.1093/gerona/56.3.m146 11253156.

74. Testa G, Liguori I, Curcio F, Russo G, Bulli G, Galizia G, et al. Multidimensional frailty evaluation in elderly outpatients with chronic heart failure: A prospective study. Eur J Prev Cardiol. 2019;26(10):1115–7. Epub 2019/02/07. doi: 10.1177/2047487319827460 30722680.

75. Pocock SJ, Ariti CA, McMurray JJ, Maggioni A, Kober L, Squire IB, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J. 2013;34(19):1404–13. Epub 2012/10/26. doi: 10.1093/eurheartj/ehs337 23095984.

76. Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, et al. The Seattle Heart Failure Model. Circulation. 2006;113(11):1424–33. doi: 10.1161/CIRCULATIONAHA.105.584102 16534009

77. Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr., Drazner MH, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;62(16):e147–239. Epub 2013/06/12. doi: 10.1016/j.jacc.2013.05.019 23747642.

78. Rich JD, Burns J, Freed BH, Maurer MS, Burkhoff D, Shah SJ. Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score: Validation of a Simple Tool for the Prediction of Morbidity and Mortality in Heart Failure With Preserved Ejection Fraction. J Am Heart Assoc. 2018;7(20):e009594. Epub 2018/10/30. doi: 10.1161/JAHA.118.009594 30371285; PubMed Central PMCID: PMC6474968.


Článek vyšel v časopise

PLOS One


2020 Číslo 1
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Svět praktické medicíny 1/2024 (znalostní test z časopisu)
nový kurz

Koncepce osteologické péče pro gynekology a praktické lékaře
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Význam metforminu pro „udržitelnou“ terapii diabetu
Autoři: prof. MUDr. Milan Kvapil, CSc., MBA

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#