Development and external validation of a nomogram to predict adverse outcomes following delayed PCI in STEMI

Abstract

Aim: Reliable prognostic tools remain limited for patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI) more than 48 h after symptom onset. This study aimed to develop and externally validate a nomogram based on routinely available in-hospital clinical variables to predict post-discharge adverse outcomes in this population.

Methods: We retrospectively analyzed data from Tongji Hospital between June 2019 and August 2022 and identified 198 STEMI patients who underwent delayed PCI as the training cohort. Independent predictors of composite adverse events, defined as all-cause mortality, nonfatal myocardial infarction, and New York Heart Association class IV heart failure, were identified using multivariate Cox proportional hazards regression. A nomogram was subsequently constructed and internally validated using bootstrap resampling. External validation was performed in an independent cohort of 599 patients treated at the Second Hospital of Lanzhou University, with a median follow-up duration of 20 months.

Results: Four variables were identified as independent predictors of adverse outcomes and incorporated into the nomogram: (1) heart rate > 83 beats per minute (hazard ratio [HR] 2.786, 95% confidence interval [CI]: 1.226-6.32, P = 0.014); (2) absence of statin therapy (HR 0.213, 95%CI: 0.064-0.71, P = 0.012); (3) intraoperative slow-flow/no-reflow phenomenon (HR 2.889, 95%CI: 1.247-6.69, P = 0.013); and (4) requirement for mechanical ventilation (HR 7.469, 95%CI: 2.57-21.70, P < 0.001). The nomogram demonstrated good discrimination and calibration in the training cohort, with a concordance index of 0.782. External validation confirmed its robust predictive performance. Patients classified as high risk exhibited significantly lower event-free survival compared with those at low risk (P < 0.0001).

Conclusion: This validated nomogram, derived from routinely collected clinical variables, provides reliable prediction of adverse outcomes in STEMI patients undergoing delayed PCI and may facilitate individualized risk stratification and optimized post-discharge management.

Keywords

ST-segment elevation myocardial infarction / delayed percutaneous coronary intervention / nomogram / risk stratification / external validation

References

[1] ST-segment elevation myocardial infarction.Nat Rev Dis Primers2019;5:40

[2] Grines CL.ST-segment elevation myocardial infarction management: great strides but still room for improvement.Eur Heart J2021;42:4550-2

[3] Stone GW,Sabik JF.Five-year outcomes after PCI or CABG for left main coronary disease.N Engl J Med2019;381:1820-30

[4] Ono M,Hara H.10-year follow-up after revascularization in elderly patients with complex coronary artery disease.J Am Coll Cardiol2021;77:2761-73

[5] Ye Z,Tang L.Predicting long-term prognosis after percutaneous coronary intervention in patients with new onset ST-elevation myocardial infarction: development and external validation of a nomogram model.Cardiovasc Diabetol2023;22:87 PMCID:PMC10103457

[6] Yang XG,Wang F.Development and validation of a prognostic nomogram for the overall survival of patients living with spinal metastases.J Neurooncol2019;145:167-76

[7] Thygesen K,Jaffe AS.Fourth universal definition of myocardial infarction (2018).Eur Heart J2019;40:237-69

[8] Virani SS,Arnold SV.2023 AHA/ACC/ACCP/ASPC/NLA/PCNA guideline for the management of patients with chronic coronary disease: a report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.Circulation2023;148:e9-119

[9] Ocak N, Yesilaras M, Kilicaslan B, Eyler Y, Mutlu İ, Kutlu M. Comparing TIMI, HEART, and GRACE risk scores to predict angiographic severity of coronary artery disease and 30-day major adverse cardiac events in emergency department patients with NSTEACS.Prehospital Disaster Med2023;38:740-8

[10] Demirkıran A,Yılmaz A.A new marker of coronary collateral flow in patients presenting with acute myocardial infarction.Eur Rev Med Pharmacol Sci2023;27:6256-63

[11] Katayama Y,Kashiwagi M.No-reflow phenomenon and in vivo cholesterol crystals combined with lipid core in acute myocardial infarction.Int J Cardiol Heart Vasc2022;38:100953 PMCID:PMC8760498

[12] Namazi M,Safi M.The no-reflow phenomenon: is it predictable by demographic factors and routine laboratory data?.Acta Biomed2021;92:e2021297

[13] Perera D,O'Kane PD.Percutaneous revascularization for ischemic left ventricular dysfunction.N Engl J Med2022;387:1351-60

[14] Tang L,Xu Y.Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention.Inflamm Res2023;72:1799-809

[15] Zhong L,Zhang J,Qian C.Risk prediction models for successful discontinuation in acute kidney injury undergoing continuous renal replacement therapy.iScience2024;27:110397 PMCID:PMC11301094

[16] Fazel R,Sankardas MA.Comparison of reperfusion strategies for ST-segment-elevation myocardial infarction: a multivariate network meta-analysis.J Am Heart Assoc2020;9:e015186

[17] Neumann FJ,Ahlsson A.2018 ESC/EACTS guidelines on myocardial revascularization.Eur Heart J.2019;40:87-165

[18] Levine GN,Blankenship JC.2015 ACC/AHA/SCAI focused update on primary percutaneous coronary intervention for patients with ST-elevation myocardial infarction: an update of the 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention and the 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Society for Cardiovascular Angiography and Interventions.Circulation2016;133:1135-47

[19] Song J,Wang W.A nomogram predicting 30-day mortality in patients undergoing percutaneous coronary intervention.Front Cardiovasc Med2022;9:897020 PMCID:PMC9428350

[20] Oesterle A,Liao JK.Pleiotropic effects of statins on the cardiovascular system.Circ Res2017;120:229-43 PMCID:PMC5467317

[21] Leoncini M,Maioli M,Bellandi F.Statin treatment before percutaneous cononary intervention.J Thorac Dis2013;5:335-42 PMCID:PMC3698299

[22] Zachariah P,Halabi KC.Epidemiology, clinical features, and disease severity in patients with coronavirus disease 2019 (COVID-19) in a children's hospital in New York City, New York.JAMA Pediatr2020;174:e202430 PMCID:PMC7270880

[23] Cockburn J,Ludman P.Percutaneous coronary intervention in octogenarians: a risk scoring system to predict 30-day outcomes in the elderly.Catheter Cardiovasc Interv2021;98:1300-7

[24] Kleinbongard P,Witting P,Klöcker N.Pleiotropic, heart rate-independent cardioprotection by ivabradine.Br J Pharmacol2015;172:4380-90 PMCID:PMC4556475

[25] Halliday BP,Lota AS.Withdrawal of pharmacological treatment for heart failure in patients with recovered dilated cardiomyopathy (TRED-HF): an open-label, pilot, randomised trial.Lancet2019;393:61-73

[26] Aro AL,Rusinaru C.Electrical risk score beyond the left ventricular ejection fraction: prediction of sudden cardiac death in the Oregon Sudden Unexpected Death Study and the Atherosclerosis Risk in Communities Study.Eur Heart J2017;38:3017-25 PMCID:PMC5837238

[27] Parikh KS,Suzuki T.Resting heart rate and long-term outcomes among the African American population: insights from the jackson heart study.JAMA Cardiol2017;2:172-80 PMCID:PMC5310994

[28] O'Hare C,Hardy R.Association of early-life factors with life-course trajectories of resting heart rate: more than 6 decades of follow-up.JAMA Pediatr2018;172:e175525 PMCID:PMC5875352

[29] Aziz F,Ibrahim KS.Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: a machine learning approach.PLoS ONE2021;16:e0254894 PMCID:PMC8328310

[30] Gao J,Ashrafi N,Alaei K.Prediction of sepsis mortality in ICU patients using machine learning methods.BMC Med Inform Decis Mak2024;24:228 PMCID:PMC11328468

[31] Tan J,He Y.Development and validation of a risk prediction model for osteoporosis in elderly patients with type 2 diabetes mellitus: a retrospective and multicenter study.BMC Geriatr2023;23:698 PMCID:PMC10604807

[32] Wang Y,Lu J,Yang Z.Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms.Ann Med2024;56:2388709 PMCID:PMC11334739