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European Journal of Medical Research
Published in: European Journal of Medical Research 1/2023

Open Access 01-12-2023 | Femoral Fracture | Research

Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures

Authors: Zhibin Xing, Yiwen Xu, Yuxuan Wu, Xiaochen Fu, Pengfei Shen, Wenqiang Che, Jing Wang

Published in: European Journal of Medical Research | Issue 1/2023

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Abstract

Background

The incidence of nonhip femoral fractures is gradually increasing, but few studies have explored the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU or developed mortality prediction models. Therefore, we chose to study this specific patient group, hoping to help clinicians improve the prognosis of patients.

Methods

This is a retrospective study based on the data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to screen risk factors. The receiver operating characteristic (ROC) curve was drawn, and the areas under the curve (AUC), net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. The consistency between the actual probability and the predicted probability was assessed by the calibration curve and Hosmer–Lemeshow goodness of fit test (HL test). Decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.

Results

The LASSO regression analysis showed that heart rate, temperature, red blood cell distribution width, blood urea nitrogen, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPSII), Charlson comorbidity index and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. The AUC, IDI and NRI of our model in the training set and validation set were better than those of the GCS and SAPSII scoring systems. The calibration curve and HL test results showed that our model prediction results were in good agreement with the actual results (P = 0.833 for the HL test of the training set and P = 0.767 for the HL test of the validation set). DCA showed that our model had a better clinical net benefit than the GCS and SAPSII scoring systems.

Conclusion

In this study, the independent risk factors for in-hospital death in patients with nonhip femoral fractures were determined, and a prediction model was constructed. The results of this study may help to improve the clinical prognosis of patients with nonhip femoral fractures.
Literature
1.
Ng AC, Drake MT, Clarke BL, Sems SA, Atkinson EJ, Achenbach SJ, Melton LJ 3rd. Trends in subtrochanteric, diaphyseal, and distal femur fractures, 1984–2007. Osteoporos Int. 2012;23(6):1721–6.CrossRefPubMed
2.
Hemmann P, Friederich M, Korner D, Klopfer T, Bahrs C. Changing epidemiology of lower extremity fractures in adults over a 15-year period—a National Hospital Discharge Registry study. BMC Musculoskelet Disord. 2021;22(1):456.CrossRefPubMedPubMedCentral
3.
Weiss RJ, Montgomery SM, Al Dabbagh Z, Jansson KA. National data of 6409 Swedish inpatients with femoral shaft fractures: stable incidence between 1998 and 2004. Injury. 2009;40(3):304–8.CrossRefPubMed
4.
Turesson E, Ivarsson K, Thorngren KG, Hommel A. Hip fractures—Treatment and functional outcome. The development over 25 years. Injury. 2018;49(12):2209–15.CrossRefPubMed
5.
Knauf T, Jensen KO, Hack J, Barthel J, Althaus H, Buecking B, Aigner R, Knobe M, Ruchholtz S, Eschbach D. Type of underlying fracture after the surgical treatment of geriatric trauma patients has no effect on mortality during intensive care treatment. Geriatr Gerontol Int. 2020;20(12):1120–5.CrossRefPubMed
6.
Li H, He Y, Huang L, Luo H, Zhu X. The nomogram model predicting overall survival and guiding clinical decision in patients with glioblastoma based on the SEER database. Front Oncol. 2020;10:1051.CrossRefPubMedPubMedCentral
7.
Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1.CrossRefPubMedPubMedCentral
8.
Vasquez MM, Hu CC, Roe DJ, Chen Z, Halonen M, Guerra S. Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application. BMC Med Res Methodol. 2016;16(1):154.CrossRefPubMedPubMedCentral
9.
10.
Tsai SHL, Lin TY, Tischler EH, Hung KH, Chen CH, Osgood GM, Fu TS, Su CY. Distal femur fractures have a higher mortality rate compared to hip fractures among the elderly: insights from the national trauma data bank. Injury. 2021;52(7):1903–7.CrossRefPubMed
11.
Ley EJ, Singer MB, Clond MA, Ley HC, Mirocha J, Bukur M, Margulies DR, Salim A. Admission heart rate is a predictor of mortality. J Trauma Acute Care Surg. 2012;72(4):943–7.CrossRefPubMed
12.
Wang Z, Chen X, Wu Y, Jiang W, Yang L, Wang H, Liu S, Liu Y. Admission resting heart rate as an independent predictor of all-cause mortality in elderly patients with hip fracture. Int J Gen Med. 2021;14:7699–706.CrossRefPubMedPubMedCentral
13.
Vardon F, Mrozek S, Geeraerts T, Fourcade O. Accidental hypothermia in severe trauma. Anaesth Crit Care Pain Med. 2016;35(5):355–61.CrossRefPubMed
14.
Dirkmann D, Hanke AA, Görlinger K, Peters J. Hypothermia and acidosis synergistically impair coagulation in human whole blood. Anesth Analg. 2008;106(6):1627–32.CrossRefPubMed
15.
Martin RS, Kilgo PD, Miller PR, Hoth JJ, Meredith JW, Chang MC. Injury-associated hypothermia: an analysis of the 2004 national trauma data bank. Shock. 2005;24(2):114–8.CrossRefPubMed
16.
Kiekkas P, Fligou F, Igoumenidis M, Stefanopoulos N, Konstantinou E, Karamouzos V, Aretha D. Inadvertent hypothermia and mortality in critically ill adults: systematic review and meta-analysis. Aust Crit Care. 2018;31(1):12–22.CrossRefPubMed
17.
Messelu MA, Tilahun AD, Beko ZW, Endris H, Belayneh AG, Tesema GA. Incidence and predictors of mortality among adult trauma patients admitted to the intensive care units of comprehensive specialized hospitals in Northwest Ethiopia. Eur J Med Res. 2023;28(1):113.CrossRefPubMedPubMedCentral
18.
Okada A, Okada Y, Narumiya H, Ishii W, Kitamura T, Iiduka R. Body temperature and in-hospital mortality in trauma patients: analysis of a nationwide trauma database in Japan. Eur J Trauma Emerg Surg. 2022;48(1):163–71.CrossRefPubMed
19.
Balvers K, Van der Horst M, Graumans M, Boer C, Binnekade JM, Goslings JC, Juffermans NP. Hypothermia as a predictor for mortality in trauma patients at admittance to the intensive care unit. J Emerg Trauma Shock. 2016;9(3):97–102.CrossRefPubMedPubMedCentral
20.
Shafi S, Elliott AC, Gentilello L. Is hypothermia simply a marker of shock and injury severity or an independent risk factor for mortality in trauma patients? Analysis of a large national trauma registry. J Trauma. 2005;59(5):1081–5.CrossRefPubMed
21.
Wang HE, Callaway CW, Peitzman AB, Tisherman SA. Admission hypothermia and outcome after major trauma. Crit Care Med. 2005;33(6):1296–301.CrossRefPubMed
22.
Salvagno GL, Sanchis-Gomar F, Picanza A, Lippi G. Red blood cell distribution width: a simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015;52(2):86–105.CrossRefPubMed
23.
Li J, Yang X, Ma J, Gong F, Chen Q. Relationship of red blood cell distribution width with cancer mortality in hospital. Biomed Res Int. 2018;2018:8914617.CrossRefPubMedPubMedCentral
24.
Lorente L, Martín MM, Abreu-González P, Pérez-Cejas A, González-Rivero AF, Ramos-Gómez L, Argueso M, Solé-Violán J, Cáceres JJ, Jiménez A, et al. Early mortality of brain infarction patients and red blood cell distribution width. Brain Sci. 2020;10(4):196.CrossRefPubMedPubMedCentral
25.
Vashistha T, Streja E, Molnar MZ, Rhee CM, Moradi H, Soohoo M, Kovesdy CP, Kalantar-Zadeh K. Red cell distribution width and mortality in hemodialysis patients. Am J Kidney Dis. 2016;68(1):110–21.CrossRefPubMedPubMedCentral
26.
Otero TM, Canales C, Yeh DD, Hou PC, Belcher DM, Quraishi SA. Elevated red cell distribution width at initiation of critical care is associated with mortality in surgical intensive care unit patients. J Crit Care. 2016;34:7–11.CrossRefPubMedPubMedCentral
27.
Hsu CC, Sun CY, Tsai CY, Chen MY, Wang SY, Hsu JT, Yeh CN, Yeh TS. Metabolism of proteins and amino acids in critical illness: from physiological alterations to relevant clinical practice. J Multidiscip Healthc. 2021;14:1107–17.CrossRefPubMedPubMedCentral
28.
Fujinaga J, Kuriyama A, Shimada N. Incidence and risk factors of acute kidney injury in the Japanese trauma population: a prospective cohort study. Injury. 2017;48(10):2145–9.CrossRefPubMed
29.
Haines RW, Lin SP, Hewson R, Kirwan CJ, Torrance HD, O’Dwyer MJ, West A, Brohi K, Pearse RM, Zolfaghari P, et al. Acute kidney injury in trauma patients admitted to critical care: development and validation of a diagnostic prediction model. Sci Rep. 2018;8(1):3665.CrossRefPubMedPubMedCentral
30.
Kellum JA, Romagnani P, Ashuntantang G, Ronco C, Zarbock A, Anders HJ. Acute kidney injury. Nat Rev Dis Primers. 2021;7(1):52.CrossRefPubMed
31.
Arihan O, Wernly B, Lichtenauer M, Franz M, Kabisch B, Muessig J, Masyuk M, Lauten A, Schulze PC, Hoppe UC, et al. Blood Urea Nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU. PLoS ONE. 2018;13(1): e0191697.CrossRefPubMedPubMedCentral
32.
Giri M, He L, Hu T, Puri A, Zheng X, Dai H, Guo S. Blood urea nitrogen is associated with in-hospital mortality in critically Ill patients with acute exacerbation of chronic obstructive pulmonary disease: a propensity score matching analysis. J Clin Med. 2022;11(22):6709.CrossRefPubMedPubMedCentral
33.
Liu EQ, Zeng CL. Blood urea nitrogen and in-hospital mortality in critically Ill patients with cardiogenic shock: analysis of the MIMIC-III database. Biomed Res Int. 2021;2021:5948636.PubMedPubMedCentral
34.
35.
Gao G, Wu X, Feng J, Hui J, Mao Q, Lecky F, Lingsma H, Maas AIR, Jiang J. China C-TBIRP: clinical characteristics and outcomes in patients with traumatic brain injury in China: a prospective, multicentre, longitudinal, observational study. Lancet Neurol. 2020;19(8):670–7.CrossRefPubMed
36.
Deng Y, Liu S, Wang Z, Wang Y, Jiang Y, Liu B. Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients. Front Med. 2022;9: 933037.CrossRef
37.
Poncet A, Perneger TV, Merlani P, Capuzzo M, Combescure C. Determinants of the calibration of SAPS II and SAPS 3 mortality scores in intensive care: a European multicenter study. Crit Care. 2017;21(1):85.CrossRefPubMedPubMedCentral
38.
Konig S, Ueberham L, Schuler E, Wiedemann M, Reithmann C, Seyfarth M, Sause A, Tebbenjohanns J, Schade A, Shin DI, et al. In-hospital mortality of patients with atrial arrhythmias: insights from the German-wide Helios hospital network of 161 502 patients and 34 025 arrhythmia-related procedures. Eur Heart J. 2018;39(44):3947–57.CrossRefPubMed
39.
Pettit NR, Wood T, Lieber M, O’Mara MS. Intensive care unit design and mortality in trauma patients. J Surg Res. 2014;190(2):640–6.CrossRefPubMed
40.
Nakai M, Iwanaga Y, Sumita Y, Wada S, Hiramatsu H, Iihara K, Kohro T, Komuro I, Kuroda T, Matoba T, et al. Associations among cardiovascular and cerebrovascular diseases: Analysis of the nationwide claims-based JROAD-DPC dataset. PLoS ONE. 2022;17(3): e0264390.CrossRefPubMedPubMedCentral
Metadata
Title
Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures
Authors
Zhibin Xing
Yiwen Xu
Yuxuan Wu
Xiaochen Fu
Pengfei Shen
Wenqiang Che
Jing Wang
Publication date
01-12-2023
Publisher
BioMed Central
Published in
European Journal of Medical Research / Issue 1/2023
Electronic ISSN: 2047-783X
DOI
https://doi.org/10.1186/s40001-023-01515-7

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