A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction

Pei J, Wang X, Xing Z, Chen P, Su W, Deng S, et al. Association between admission systolic blood pressure and major adverse cardiovascular events in patients with acute myocardial infarction. PLoS ONE. 2020;15: e0234935.

Article  CAS  PubMed Central  PubMed  Google Scholar 

Harati H, Shamsi A, Firouzkouhi Moghadam M, Seyed Zadeh FS, Ghazi A. The mortality rate of myocardial infraction patients with and without opium dependent. Int J High Risk Behav Addict. 2015;4: e22576.

Article  PubMed Central  PubMed  Google Scholar 

Rohani A, Akbari V, Moradian K, Malekzade J. Combining white blood cell count and thrombosis for predicting in-hospital outcomes after acute myocardial infraction. J Emerg Trauma Shock. 2011;4:351.

Article  PubMed Central  PubMed  Google Scholar 

Dakik HA, Chehab O, Eldirani M, Sbeity E, Karam C, Abou Hassan O, et al. A new index for pre-operative cardiovascular evaluation. J Am Coll Cardiol. 2019;73:3067–78.

Article  PubMed  Google Scholar 

Muller G, Flecher E, Lebreton G, Luyt C-E, Trouillet J-L, Bréchot N, et al. The ENCOURAGE mortality risk score and analysis of long-term outcomes after VA-ECMO for acute myocardial infarction with cardiogenic shock. Intensive Care Med. 2016;42:370–8.

Article  PubMed  Google Scholar 

Valley TS, Sjoding MW, Goldberger ZD, Cooke CR. ICU use and quality of care for patients with myocardial infarction and heart failure. Chest. 2016;150:524–32.

Article  PubMed Central  PubMed  Google Scholar 

Valley TS, Iwashyna TJ, Cooke CR, Sinha SS, Ryan AM, Yeh RW, et al. Intensive care use and mortality among patients with ST elevation myocardial infarction: retrospective cohort study. BMJ. 2019;365: l1927.

Article  PubMed Central  PubMed  Google Scholar 

Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute physiology and chronic health evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med. 2006;34:1297–310.

Article  PubMed  Google Scholar 

Zimmerman JE, Kramer AA, McNair DS, Malila FM, Shaffer VL. Intensive care unit length of stay: Benchmarking based on acute physiology and chronic health evaluation (APACHE) IV. Crit Care Med. 2006;34:2517–29.

Article  PubMed  Google Scholar 

Nassar AP, Mocelin AO, Nunes ALB, Giannini FP, Brauer L, Andrade FM, et al. Caution when using prognostic models: a prospective comparison of 3 recent prognostic models. J Crit Care. 2012;27:423.e1-423.e7.

Article  PubMed  Google Scholar 

Fox KAA, Dabbous OH, Goldberg RJ, Pieper KS, Eagle KA, Van de Werf F, et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ. 2006;333:1091.

Article  PubMed Central  PubMed  Google Scholar 

Antman EM, Cohen M, Bernink PJ, McCabe CH, Horacek T, Papuchis G, et al. The TIMI risk score for unstable angina/non-ST elevation MI: a method for prognostication and therapeutic decision making. JAMA. 2000;284:835.

Article  CAS  PubMed  Google Scholar 

Granger CB, Goldberg RJ, Dabbous O, Pieper KS, Eagle KA, Cannon CP, et al. Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med. 2003;163:2345.

Article  PubMed  Google Scholar 

Wu C, Gao XJ, Zhao YY, Yang JG, Yang YJ, Xu HY, et al. Prognostic value of TIMI and GRACE risk scores for in-hospital mortality in Chinese patients with non-ST-segment elevation myocardial infarction]. Zhonghua Xin Xue Guan Bing Za Zhi. 2019;47:297–304.

CAS  PubMed  Google Scholar 

Jentzer JC, Anavekar NS, Bennett C, Murphree DH, Keegan MT, Wiley B, et al. Derivation and validation of a novel cardiac intensive care unit admission risk score for mortality. J Am Heart Assoc. 2019;8: e013675.

Article  PubMed Central  PubMed  Google Scholar 

Lazzeri C, Valente S, Chiostri M, Gensini GF. Clinical significance of lactate in acute cardiac patients. World J Cardiol. 2015;7:483–9.

Article  PubMed Central  PubMed  Google Scholar 

Porto I, Mattesini A, D’Amario D, Sorini Dini C, Della Bona R, Scicchitano M, et al. Blood lactate predicts survival after percutaneous implantation of extracorporeal life support for refractory cardiac arrest or cardiogenic shock complicating acute coronary syndrome: insights from the CareGem registry. Intern Emerg Med. 2021;16:463–70.

Article  PubMed  Google Scholar 

Rigamonti F, Montecucco F, Boroli F, Rey F, Gencer B, Cikirikcioglu M, et al. The peak of blood lactate during the first 24h predicts mortality in acute coronary syndrome patients under extracorporeal membrane oxygenation. Int J Cardiol. 2016;221:741–5.

Article  PubMed  Google Scholar 

Baysan M, Baroni GD, van Boekel AM, Steyerberg EW, Arbous MS, van der Bom JG. The added value of lactate and lactate clearance in prediction of in-hospital mortality in critically ill patients with sepsis. Crit Care Explor. 2020;2: e0087.

Article  PubMed Central  PubMed  Google Scholar 

Cluntun AA, Badolia R, Lettlova S, Parnell KM, Shankar TS, Diakos NA, et al. The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure. Cell Metab. 2021;33:629-648.e10.

Article  CAS  PubMed  Google Scholar 

Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350: g7594.

Article  PubMed  Google Scholar 

Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU collaborative research database, a freely available multi-center database for critical care research. Sci Data. 2018;5: 180178.

Article  PubMed Central  PubMed  Google Scholar 

Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med. 2016;44:368–74.

Article  PubMed Central  PubMed  Google Scholar 

Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, 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.

Article  PubMed  Google Scholar 

Farrar D, Fairley L, Santorelli G, Tuffnell D, Sheldon TA, Wright J, et al. Association between hyperglycaemia and adverse perinatal outcomes in south Asian and white British women: analysis of data from the born in Bradford cohort. Lancet Diabetes Endocrinol. 2015;3:795–804.

Article  CAS  PubMed Central  PubMed  Google Scholar 

Newgard CD, Haukoos JS. Advanced statistics: missing data in clinical research-part 2: multiple imputation. Acad Emerg Med. 2007;14:669–78.

PubMed  Google Scholar 

Haukoos JS, Newgard CD. Advanced statistics: missing data in clinical research-part 1: an introduction and conceptual framework. Acad Emerg Med. 2007;14:662–8.

PubMed  Google Scholar 

Su Y-S, Gelman A, Hill J, Yajima M. Multiple imputation with diagnostics (mi) inR: opening windows into the black box. J Stat Softw. 2011;45:1–31.

Article  Google Scholar 

Tahmassebi A, Wengert GJ, Helbich TH, Bago-Horvath Z, Alaei S, Bartsch R, et al. Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients. Invest Radiol. 2019;54:110–7.

Article  PubMed Central  PubMed  Google Scholar 

Chen T, Guestrin C. XGBoost. Proc 22nd ACM SIGKDD Int Conf Knowl Discov Data Min [Internet]. New York, NY, USA: Association for Computing Machinery; 2016 [cited 2022 Jun 24]. pp. 785–94. Available from: https://dl.acm.org/doi/pdf/https://doi.org/10.1145/2939672.2939785

Melamed A, Margul DJ, Chen L, Keating NL, Del Carmen MG, Yang J, et al. Survival after minimally invasive radical hysterectomy for early-stage cervical cancer. N Engl J Med. 2018;379:1905–14.

Article  PubMed Central  PubMed  Google Scholar 

Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26:1364–70.

Article  PubMed  Google Scholar 

Silva TB, Oliveira CZ, Faria EF, Mauad EC, Syrjänen KJ, Carvalho AL. Development and validation of a nomogram to estimate the risk of prostate cancer in Brazil. Anticancer Res. 2015;35:2881–6.

PubMed  Google Scholar 

Li W, Xie B, Qiu S, Huang X, Chen J, Wang X, et al. Non-lab and semi-lab algorithms for screening undiagnosed diabetes: a cross-sectional study. EBioMedicine. 2018;35:307–16.

Article  PubMed Central  PubMed  Google Scholar 

Dong D, Tang L, Li Z-Y, Fang M-J, Gao J-B, Shan X-H, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol. 2019;30:431–8.

Article  CAS  PubMed Central  PubMed  Google Scholar 

Chen L, Zheng H, Wang S. Prediction model of emergency mortality risk in patients with acute upper gastrointestinal bleeding: a retrospective study. PeerJ. 2021;9: e11656.

Article  PubMed Central  PubMed  Google Scholar 

Van Calster B, Wynants L, Verbeek JFM, Verbakel JY, Christodoulou E, Vickers AJ, et al. Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol. 2018;74:796–804.

Article  PubMed Central  PubMed  Google Scholar 

Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux PJ, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. 2017;318:1377.

Article  PubMed 

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