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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2024

Open Access 01-12-2024 | Endocarditis | Research

Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis

Authors: Dongyang Che, Jinlin Hu, Jialiang Zhu, Jun Lyu, Xiaoshen Zhang

Published in: BMC Medical Informatics and Decision Making | Issue 1/2024

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Abstract

Background

Infective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in ICU individuals diagnosed with infective endocarditis.

Methods

Retrospectively collected clinical data of patients with IE admitted to the ICU in the MIMIC IV database were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential hazards. A logistic regression model incorporating multiple factors was established, and a dynamic nomogram was generated to facilitate predictions. To assess the classification performance of the model, an ROC curve was generated, and the AUC value was computed as an indicator of its diagnostic accuracy. The model was subjected to calibration curve analysis and the Hosmer–Lemeshow (HL) test to assess its goodness of fit. To evaluate the clinical relevance of the model, decision-curve analysis (DCA) was conducted.

Results

The research involved a total of 676 patients, who were divided into two cohorts: a training cohort comprising 473 patients and a validation cohort comprising 203 patients. The allocation ratio between the two cohorts was 7:3. Based on the independent predictors identified through LASSO regression, the final selection for constructing the prediction model included five variables: lactate, bicarbonate, white blood cell count (WBC), platelet count, and prothrombin time (PT). The nomogram model demonstrated a robust diagnostic ability in both the cohorts used for training and validation. This is supported by the respective area under the curve (AUC) values of 0.843 and 0.891. The results of the calibration curves and HL tests exhibited acceptable conformity between observed and predicted outcomes. According to the DCA analysis, the nomogram model demonstrated a notable overall clinical advantage compared to the APSIII and SAPSII scoring systems.

Conclusions

The nomogram developed during the study proved to be highly accurate in forecasting the mortality of patients with IE during hospitalization in the ICU. As a result, it may be useful for clinicians in decision-making and treatment.
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Literature
1.
2.
Chen H, Zhan Y, Zhang K, Gao Y, Chen L, Zhan J, Chen Z, Zeng Z. The global, regional, and national burden and trends of infective endocarditis from 1990 to 2019: results from the global burden of disease study 2019. Front Med (Lausanne). 2022;9:774224.CrossRefPubMed
3.
Duval X, Delahaye F, Alla F, Tattevin P, Obadia JF, Le Moing V, Doco-Lecompte T, Celard M, Poyart C, Strady C, et al. Temporal trends in infective endocarditis in the context of prophylaxis guideline modifications: three successive population-based surveys. J Am Coll Cardiol. 2012;59(22):1968–76.CrossRefPubMed
4.
Krajinovic V, Ivancic S, Gezman P, Barsic B. Association between cardiac surgery and mortality among patients with infective endocarditis complicated by sepsis and septic shock. Shock. 2018;49(5):536–42.CrossRefPubMed
5.
Habib G, Erba PA, Iung B, Donal E, Cosyns B, Laroche C, Popescu BA, Prendergast B, Tornos P, Sadeghpour A, et al. Clinical presentation, aetiology and outcome of infective endocarditis. Results of the ESC-EORP EURO-ENDO (European infective endocarditis) registry: a prospective cohort study. Eur Heart J. 2019;40(39):3222–32.CrossRefPubMed
6.
Habib G, Lancellotti P, Antunes MJ, Bongiorni MG, Casalta JP, Del Zotti F, Dulgheru R, El Khoury G, Erba PA, Iung B, et al. 2015 ESC guidelines for the management of infective endocarditis: the task force for the management of infective endocarditis of the European Society of Cardiology (ESC). Endorsed by: European Association for Cardio-thoracic surgery (EACTS), the European Association of Nuclear Medicine (EANM). Eur Heart J. 2015;36(44):3075–128.CrossRefPubMed
7.
Baumgartner H, Falk V, Bax JJ, De Bonis M, Hamm C, Holm PJ, Iung B, Lancellotti P, Lansac E, Rodriguez Munoz D, et al. 2017 ESC/EACTS guidelines for the management of valvular heart disease. Eur Heart J. 2017;38(36):2739–91.CrossRefPubMed
8.
9.
Park SY. Nomogram: an analogue tool to deliver digital knowledge. J Thorac Cardiov Sur. 2018;155(4).
10.
11.
Zhou J, Liu Y, Zhang Y, Li Q, Cao Y. Modeling tumor evolutionary dynamics to predict clinical outcomes for patients with metastatic colorectal cancer: a retrospective analysis. Cancer Res. 2020;80(3):591–601.CrossRefPubMed
12.
13.
Pettersson GB, Coselli JS, Hussain ST, Griffin B, Blackstone EH, Gordon SM, LeMaire SA, Woc-Colburn LE, Infective AST, Comm W. 2016 the American Association for Thoracic Surgery (AATS) consensus guidelines: surgical treatment of infective endocarditis: executive summary. J Thorac Cardiov Sur. 2017;153(6):1241–U1581.CrossRef
14.
Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991;100(6):1619–36.CrossRefPubMed
15.
Le Gall JR, Lemeshow S, Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270(24):2957–63.CrossRefPubMed
16.
Glance LG, Osler TM, Dick AW. Identifying quality outliers in a large, multiple-institution database by using customized versions of the simplified acute physiology score II and the mortality probability model II. Crit Care Med. 2002;30(9):1995–2002.CrossRefPubMed
17.
Jentzer JC, van Diepen S, Murphree DH, Ismail AS, Keegan MT, Morrow DA, Barsness GW, Anavekar NS. Admission diagnosis and mortality risk prediction in a contemporary cardiac intensive care unit population. Am Heart J. 2020;224:57–64.CrossRefPubMed
18.
Hernandez G, Castro R, Romero C, de la Hoz C, Angulo D, Aranguiz I, Larrondo J, Bujes A, Bruhn A. Persistent sepsis-induced hypotension without hyperlactatemia: is it really septic shock? J Crit Care. 2011;26(4).
19.
Jansen TC, van Bommel J, Mulder PG, Lima AP, van der Hoven B, Rommes JH, Snellen FTF, Bakker J. Prognostic value of blood lactate levels: does the clinical diagnosis at admission matter? J Trauma. 2009;66(2):377–85.PubMed
20.
Hernandez G, Bellomo R, Bakker J. The ten pitfalls of lactate clearance in sepsis. Intensive Care Med. 2019;45(1):82–5.CrossRefPubMed
21.
Nichol A, Bailey M, Egi M, Pettila V, French C, Stachowski E, Reade MC, Cooper DJ, Bellomo R. Dynamic lactate indices as predictors of outcome in critically ill patients. Crit Care. 2011;15(5).
22.
Haas SA, Lange T, Saugel B, Petzoldt M, Fuhrmann V, Metschke M, Kluge S. Severe hyperlactatemia, lactate clearance and mortality in unselected critically ill patients. Intensive Care Med. 2016;42(2):202–10.CrossRefPubMed
23.
Kraut JA, Madias NE. Metabolic acidosis: pathophysiology, diagnosis and management. Nat Rev Nephrol. 2010;6(5):274–85.CrossRefPubMed
24.
Ellis MF. Use of bicarbonate in patients with metabolic acidosis. Crit Care Nurse. 2015;35(5):73–5.CrossRefPubMed
25.
Kraut JA, Madias NE. Treatment of acute metabolic acidosis: a pathophysiologic approach. Nat Rev Nephrol. 2012;8(10):589–601.CrossRefPubMed
26.
Liesenborghs L, Meyers S, Vanassche T, Verhamme P. Coagulation: at the heart of infective endocarditis. J Thromb Haemost. 2020;18(5):995–1008.CrossRefPubMed
27.
Papareddy P, Rydengard V, Pasupuleti M, Walse B, Morgelin M, Chalupka A, Malmsten M, Schmidtchen A. Proteolysis of human thrombin generates novel host defense peptides. PLoS Pathog. 2010;6(4):e1000857.CrossRefPubMedPubMedCentral
28.
Liesenborghs L, Verhamme P, Vanassche T. Staphylococcus aureus, master manipulator of the human hemostatic system. J Thromb Haemost. 2018;16(3):441–54.CrossRefPubMed
29.
Sullam PM, Frank U, Yeaman MR, Tauber MG, Bayer AS, Chambers HF. Effect of thrombocytopenia on the early course of streptococcal endocarditis. J Infect Dis. 1993;168(4):910–4.CrossRefPubMed
30.
Dall L, Miller T, Herndon B, Diez I, Dew M. Platelet depletion and severity of streptococcal endocarditis. Can J Infect Dis. 1998;9(6):359–66.PubMedPubMedCentral
31.
Wuescher LM, Takashima A, Worth RG. A novel conditional platelet depletion mouse model reveals the importance of platelets in protection against staphylococcus aureus bacteremia. J Thromb Haemost. 2015;13(2):303–13.CrossRefPubMed
32.
Menard CE, Kumar A, Houston DS, Turgeon AF, Rimmer E, Houston BL, Doucette S, Zarychanski R. Evolution and impact of thrombocytopenia in septic shock: a retrospective cohort study. Crit Care Med. 2019;47(4):558–65.CrossRefPubMed
33.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818–29.CrossRefPubMed
34.
Baddour LM, Wilson WR, Bayer AS, Fowler VG Jr., Tleyjeh IM, Rybak MJ, Barsic B, Lockhart PB, Gewitz MH, Levison ME, et al. Infective endocarditis in adults: diagnosis, antimicrobial therapy, and management of complications: a scientific statement for healthcare professionals from the American Heart Association. Circulation. 2015;132(15):1435–86.CrossRefPubMed
35.
Martinez-Selles M, Munoz P, Arnaiz A, Moreno M, Galvez J, Rodriguez-Roda J, de Alarcon A, Garcia Cabrera E, Farinas MC, Miro JM, et al. Valve surgery in active infective endocarditis: a simple score to predict in-hospital prognosis. Int J Cardiol. 2014;175(1):133–7.CrossRefPubMed
36.
De Feo M, Cotrufo M, Carozza A, De Santo LS, Amendolara F, Giordano S, Della Ratta EE, Nappi G, Della Corte A. The need for a specific risk prediction system in native valve infective endocarditis surgery. ScientificWorldJournal. 2012;2012:307571.
37.
Olmos C, Vilacosta I, Habib G, Maroto L, Fernandez C, Lopez J, Sarria C, Salaun E, Di Stefano S, Carnero M, et al. Risk score for cardiac surgery in active left-sided infective endocarditis. Heart. 2017;103(18):1435–42.CrossRefPubMed
38.
Gatti G, Perrotti A, Obadia JF, Duval X, Iung B, Alla F, Chirouze C, Selton-Suty C, Hoen B, Sinagra G et al. Simple scoring system to predict in-hospital mortality after surgery for infective endocarditis. J Am Heart Assoc. 2017;6(7).
39.
Gatti G, Benussi B, Gripshi F, Della Mattia A, Proclemer A, Cannata A, Dreas L, Luzzati R, Sinagra G, Pappalardo A. A risk factor analysis for in-hospital mortality after surgery for infective endocarditis and a proposal of a new predictive scoring system. Infection. 2017;45(4):413–23.CrossRefPubMed
40.
Habib G, Erba PA, Iung B, Donal E, Cosyns B, Laroche C, Popescu BA, Prendergast B, Tornos P, Sadeghpour A, et al. Clinical presentation, aetiology and outcome of infective endocarditis. Results of the ESC-EORP EURO-ENDO (European infective endocarditis) registry: a prospective cohort study. Eur Heart J. 2019;40(39):3222–+.CrossRefPubMed
Metadata
Title
Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis
Authors
Dongyang Che
Jinlin Hu
Jialiang Zhu
Jun Lyu
Xiaoshen Zhang
Publication date
01-12-2024
Publisher
BioMed Central
Keyword
Endocarditis
Published in
BMC Medical Informatics and Decision Making / Issue 1/2024
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-024-02482-7

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