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

European Journal of Medical Research volume 28, Article number: 539 (2023) Cite this article

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.

Background

Although the incidence of nonhip femoral fractures is much lower than that of proximal femoral fractures, studies have shown that this incidence is gradually increasing [1, 2]. The main causes of hospitalization for nonhip femoral fractures are traffic accidents and falls at the same level, while the main factors for hip fractures include decreased bone mineral density and increased fall rates [3]. According to statistics, 51% of nonhip femoral fractures are caused by severe trauma, and the main fracture type is a shaft fracture [1]. Patients with nonhip femoral fractures often exhibit multiple accompanying injuries of the whole body or injuries of important organs and are more likely to experience complications, such as hemorrhagic shock, which endanger patients’ lives.

The short-term and long-term mortality and functional outcomes of patients with hip fracture after intensive care unit (ICU) treatment have been well studied. However, we know little about other trauma patients in the ICU [4]. A study showed that hip fracture is the most common type of injury among trauma patients in the ICU (47.6%), and the proportion of patients with nonhip femoral fracture is 2.2% [5]. Moreover, to our knowledge, there are few studies exploring the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU, and the development of a mortality prediction model is needed. Therefore, we chose to study this specific patient group.

We established a predictive model based on routine clinical and laboratory indicators to ensure that it is easy to implement in clinical work. Nomograms have been proven to be an intuitive and easy-to-use tool for clinical personalized risk assessment by integrating potential risk factors and are therefore commonly used in medical research and clinical practice [6]. This study, based on the MIMIC-IV database, aimed to establish a predictive model for in-hospital mortality in patients with nonhip femoral fractures.

Materials and methods

Data source

The data used in this retrospective study were collected from the MIMIC-IV version 2.0 database of the intensive care medical information market. An update of the MIMIC-III database, this database contains more than 40,000 unique patients admitted to the ICU of Beth Israel Deaconess Medical Center from 2008 to 2019 [7]. To protect patients’ privacy, all private information in the database repository has been deleted. Therefore, the requirements for informed consent and ethical approval in this study were waived. According to the data usage agreement, ZHI bin XING completed the training for protecting human research participants (certificate number: 48590713) and was responsible for the acquisition and analysis of the research data.

Study population

We use the structured query language in Navicat Premium Version 15 to extract patient-related information from the MIMIC-IV database. Using the International Classification of Diseases (ICD) codes (ICD_CODE) 72,709, 71,949, 72,610, 72,939, 80,165, 5731, 1632, 3501, 3609, 36,102, 3639, 36,589, 36,800, 36,914, 36,916, 37,146, 37,557, 37,561, 33,379, 34,541, 85,233, 8631, 86,803, 90,183, 90,232, 90,282, 9071, 9181, 9391, 94,240, 94,444, 94,865, 9760, 9894, 99,591, 99,669, 99,686, and 9972, we obtained 5519 patients with nonhip femoral fractures. Patients who met the following criteria were excluded: patients who were not admitted to the ICU for the first time (n = 4084) and patients aged < 18 and > 89 (n = 101). Finally, 1334 patients were included in the study (Fig. 1).

Fig. 1
figure 1

Workflow of the study. ICU, Intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care IV; LASSO, Least absolute shrinkage and selection operator; ROC, Receiver operating characteristic; AUC, Area under the receiver operating characteristic curve; NRI, Net reclassification improvement; IDI, Integrated discrimination improvement; HL test, Hosmer‒Lemeshow test; DCA, Decision curve analysis

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Data extraction

Using the patient's hadm_id and stay_id, we extracted the following data: demographic data, vital signs, laboratory test results, comorbidities, and scoring system. Demographic data included sex and age. Vital signs on the first day of ICU admission included heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), body temperature, and respiratory rate (RR). Laboratory parameters on the first day included SpO2, glucose, red blood cell (RBC) count, red blood cell distribution width (RDW), hemoglobin, hematocrit, platelet count, white blood cell (WBC) count, anion gap, bicarbonate, blood urea nitrogen (BUN), calcium, chloride, creatinine, sodium, potassium, prothrombin time (PT), and partial thromboplastin time (PTT). Comorbidities included Charlson comorbidity index (CCI), congestive heart failure, myocardial infarct, cerebrovascular disease, chronic pulmonary disease, dementia, diabetes, and renal disease. The scoring system included the Glasgow Coma Scale (GCS) and Simplified Acute Physiology Score II (SAPSII). Variables with a missing value proportion > 20% were eliminated. For variables with a missing value ratio < 20%, we used the mice package in R for multiple interpolation.

Statistical analysis

We randomly divided the patients with nonhip femoral fractures into a training set and a verification set at a ratio of 7:3. The Shapiro–Wilk test was used to determine whether a continuous variable had a normal distribution. If a continuous variable was normally distributed, it was described as the mean and standard deviation. If a continuous variable was not normally distributed, it was described as the median and interquartile range, and Wilcoxon’s rank-sum test was selected for comparison between two groups. Categorical variables were expressed as frequency/percentage, and the Chi-square test or Fisher’s exact test was used to compare different groups. The least absolute shrinkage and selection operator (LASSO) was developed in 1996, and is particularly suitable for variable selection among many variables for the prediction of an outcome [8]. LASSO can be used to reduce the coefficient of irrelevance while retaining important variables and improve both prediction accuracy and interpretation [9]. The independent risk factors for death in patients with nonhip femoral fracture were determined by LASSO regression, and the results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs). According to the results of cross-validation, we chose the largest λ value with an average error within one standard deviation to determine the variables included in the model. Finally, these variables were used to construct a nomogram to predict the in-hospital mortality of patients with nonhip femoral fractures. ROC curves and AUC were used to evaluate the discrimination of the model compared with those of GCS and SAPSII. In addition, the IDI and NRI were used to calculate the performance improvement of the nomogram compared to the GCS and SAPSII scoring systems. We also constructed a calibration curve and performed the Hosmer‒Lemeshow test (HL test) to evaluate the consistency between the predicted risk and the actual risk. DCA was used to evaluate the net clinical benefit and the clinical applicability of the nomogram. All statistical analyses were performed in R (version 4.2.2). A two-sided P < 0.05 was considered statistically significant.

Results

Baseline characteristics of the patients

A total of 1334 patients were included in this study and divided into a survival group (n = 1202) and a nonsurvival group (n = 132) according to the survival outcome. The characteristics of the two groups are shown in Table 1. Compared with the survival group, the patients in the nonsurvival group were older, had a faster heart rate, and had lower blood pressure and body temperature. In addition, the laboratory indexes of RBC count, hemoglobin, hematocrit and bicarbonate in the nonsurvival group were lower, and RDW, anion gap, BUN, creatinine, potassium, PT and PTT were higher. Moreover, the GCS score of the nonsurvival group was lower, the SAPSII and CCI were higher, and the prevalence of cerebrovascular disease and renal disease was higher.

Table 1 Baseline characteristics of in-hospital nonsurvival and survival groups
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Risk factor screening and nomogram development

LASSO regression analysis and cross-validation were applied to identify independent risk factors for in-hospital death in patients with nonhip femoral fractures (Fig. 2). The results demonstrated that HR, temperature, RDW, BUN, GCS score, SAPSII, CCI and cerebrovascular disease were independent risk factors. HR (OR: 1.04; 95% CI 1.02–1.05; P < 0.001), RDW (OR: 1.15; 95% CI 1.05–1.25; P = 0.003), BUN (OR: 1.01; 95% CI 1.00–1.02; P = 0.032), SAPSII (OR: 1.03; 95% CI 1.01–1.05; P < 0.001), CCI (OR: 1.17; 95% CI 1.07–1.27; P < 0.001), cerebrovascular disease (OR: 1.97; 95% CI 1.12–3.39; P = 0.016) were risk factors for hospital death in patients with nonhip femoral fracture, and temperature (OR: 0.55; 95% CI 0.39–0.76; P < 0.001) and GCS score (OR: 0.85; 95% CI 0.80–0.90; P < 0.001) were protective factors (Table 2). Based on these results, a nomogram was constructed to predict the in-hospital mortality of patients with nonhip femoral fractures in the ICU (Fig. 3).

Fig. 2
figure 2

Clinical variables were selected using the lasso logistic regression model. a Tuning parameter (λ) selection using LASSO penalized logistic regression with fivefold cross-validation. b LASSO coefficient profiles of the radiomic features

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Table 2 Multiple regression model based on LASSO regression results
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Fig. 3
figure 3

Nomogram for predicting the risk of in-hospital mortality in patients with nonhip femoral fractures in the ICU. GCS, Glasgow Coma Scale; SAPSII, Simplified Acute Physiology Score II; BUN, Blood urea nitrogen; RDW, Red blood cell distribution width; HR, Heart rate. *means p < 0.05,**means p < 0.01,***means p < 0.001

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Verification of the nomogram

The predictive ability of our model and the GCS and SAPSII scoring systems for in-hospital mortality in patients with nonhip femoral fractures were compared and analyzed. As shown in Fig. 4, the AUCs of our model in the training set and the verification set were 0.891 (95% CI   0.861–0.922) and 0.830 (95% CI   0.767–0.893), respectively, which were higher than those of the GCS and SAPSII scoring systems. The AUCs of each system were further compared by the DeLong test. In the training set, the AUC of our model significantly differed from that of the GCS (P < 0.001) and SAPSII (P < 0.001) scoring systems. In the validation set, the AUC of our model significantly differed from that of the GCS score (P < 0.001). Compared with the SAPSII scoring system, our model showed similar performance (P = 0.096). In addition, compared with the GCS score, the NRI values of the nomogram in the training set and verification set were 1.131 (95% CI   0.950–1.311) and 0.860 (95% CI 0.572–1.149), respectively. The corresponding IDI values were 0.193 (95% CI   0.142–0.245) (P < 0.001) and 0.122 (95% CI  0.06–0.184) (P < 0.001). Compared with the SAPSII system, the NRI values of the nomogram in the training set and verification set were 0.985 (95% CI   0.800–1.170) and 0.732 (95% CI  0.431–1.032), respectively. The corresponding IDI values were 0.120 (95% CI  0.077–0.164) (P < 0.001) and 0.056 (95% CI 0.011–0.101) (P = 0.015). These results showed that our nomogram had better recognition ability and was superior to other commonly used scoring systems. In addition, the calibration chart and HL test results showed that the predicted results of our model were consistent with the actual results (HL test of the training set, P = 0.833; HL test of the verification set, P = 0.767) (Fig. 5). Finally, a DCA curve was performed to illustrate the clinical applicability of the nomogram and compare it with the GCS and SAPSII scoring systems (Fig. 6). The X-axis indicates the threshold probability for in-hospital death, and the Y-axis indicates the net benefit to stratify the risk of patients. The results showed that our model had better clinical net income than the GCS and SAPSII scoring systems if the threshold probability was less than 40%

Fig. 4
figure 4

Receiver operating characteristic curve of the established nomogram, GCS and SAPSII. a Training cohort, b Verification cohort. GCS, Glasgow Coma Scale; SAPSII, Simplified Acute Physiology Score II; AUC, Area under the receiver operating characteristic curve

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Fig. 5
figure 5

Calibration curve of the established nomogram. a Training cohort, b Verification cohort

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Fig. 6
figure 6

Decision curve analysis of the established nomogram, GCS, and SAPS II. a Training cohort, b Verification cohort. GCS, Glasgow Coma Scale; SAPSII, Simplified Acute Physiology Score II

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Discussion

Our study showed that HR, temperature, RDW, BUN, GCS score, SAPSII, CCI and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. Based on these results, we constructed a nomogram to predict in-hospital mortality in patients with nonhip femoral fractures and used the AUC, NRI, IDI, calibration curve, HL test, DCA curve and other indicators to confirm the effectiveness of the nomogram.

According to one study, compared with patients with hip fracture, the in-hospital mortality rate of patients with distal femoral fracture increased significantly (8.3% vs. 6.7%) [10]. According to our statistics, the in-hospital mortality rate of patients with nonhip femoral fractures is 9.9%, which is basically consistent with a previous study. Our study showed that the HR of patients in the nonsurvival group was significantly higher than that in the survival group (P < 0.001), which may be related to the lower blood pressure of patients in the nonsurvival group. A study showed that patients with moderate and severe trauma had the lowest mortality rate when their HR was 70–89 beats/min. When the HR is < 70 or > 90, the mortality of patients increases [11]. Our study also showed that a higher HR is a risk factor for hospital death in patients with nonhip femoral fractures, which is consistent with previous research results [12].

In the management of trauma patients, body temperature is an important vital sign. All chemical reactions occurring within the human body exhibit a direct correlation with the prevailing body temperature. The manifestation of hypothermia entails numerous ramifications, including the potential induction of arrhythmias and its contribution to trauma-induced coagulopathy. Furthermore, hypothermia significantly heightens the susceptibility of patients to pneumonia. There is a higher likelihood of hypothermia among patients who have sustained severe injuries [13]. Research has demonstrated that hypothermia, even at regular pH levels, can extend the duration of clotting, induce dysfunction in coagulation, and elevate the mortality rate among individuals suffering from trauma [14, 15]. Numerous studies have shown that lower body temperature at admission is associated with increased in-hospital mortality in critically ill trauma patients [16,17,18]. In this study, the body temperature of patients in the nonsurvival group was lower than that of patients in the survival group. Moreover, multiple logistic regression analysis showed that relatively high body temperature was a protective factor for patients with nonhip femoral fractures. Previous studies have also shown that hypothermia has no significant protective effect on trauma patients, and hypothermia is independently associated with an increased risk of death in patients after major trauma [19,20,21].

RDW is a parameter reflecting the heterogeneity of red blood cell volume, which is traditionally used for the differential diagnosis of anemia. An increase in RDW reflects a serious disorder of erythrocyte homeostasis involving impaired erythropoiesis and abnormal erythrocyte survival, which may be related to shortened telomere length, oxidative stress, inflammation and abnormal erythropoietin function [22]. Studies have shown that an increase in RDW is related to an increase in the short-term and long-term mortality of many diseases, such as hemodialysis, ischemic stroke, cancer, acute pulmonary embolism and other diseases [23,24,25]. In addition, a study reported that an increase in RDW was related to an increase in mortality of patients in the surgical ICU [26]. Our study also suggested that elevated RDW was an independent risk factor for death in patients with nonhip femoral fractures. Therefore, patients with elevated RDW values should receive more attention to improve their clinical results.

BUN is the main end product of human protein metabolism, which is mainly produced by the liver and excreted by the kidney. When too much protein decomposes or the glomerular filtration rate decreases substantially, the level of BUN increases. On the one hand, severe trauma will accelerate the catabolism of protein, which will lead to an increase in mortality [27]. On the other hand, trauma patients admitted to the ICU are prone to acute renal injury, with a probability of 19.6%, which will lead to a decrease in glomerular filtration rate and an increase in BUN level and increase the hospitalization time and mortality of patients [28,29,30]. In addition, high BUN levels at admission have been shown to be significantly associated with in-hospital mortality in ICU patients, which is consistent with the results of this study [31,32,33].

The GCS score is usually used to evaluate the severity of consciousness disorder. The lower the GCS score is, the more serious the consciousness damage, and a lower GCS score can be used to predict the hospitalization mortality of patients with acute ischemic stroke [34]. A multicenter observational study showed that GCS score is an important predictor of hospital death in patients with traumatic brain injury [35]. It has been reported that GCS score is highly correlated with adverse outcomes in ICU patients [36]. In our study, GCS score was negatively correlated with the risk of hospitalization death in patients with nonhip femoral fractures, which is consistent with previous studies.

In this study, we found that the SAPSII of the nonsurvival group was significantly higher than that of the survival group and was positively correlated with in-hospital mortality. Since SAPSII is a predictive tool widely used to assess mortality, this finding is not surprising [37]. In addition, the CCI in the nonsurvival group was 7.00 [5.00;10.0], which was higher than the CCI of 5.00 [3.00;7.00] in the survival group (P < 0.001). Our findings are consistent with previous studies showing that CCI is an independent predictor of in-hospital mortality in critically ill patients [38, 39].

A nationwide study in Japan showed that compared with patients with noncerebrovascular diseases, patients with cerebrovascular diseases had higher in-hospital mortality [40]. In this study, the proportion of patients with cerebrovascular disease in the nonsurvival group was 22.7%, and the proportion of patients with cerebrovascular disease in the survival group was relatively small, only 9.40%. The incidence of cerebrovascular diseases in the nonsurvival group significantly differed from that in the survival group (P < 0.001).

However, our study has some limitations. First, since the data were extracted from the MIMIC-IV database and belong to a single-center retrospective study, potential selection bias is inevitable, so data from different medical institutions are needed for external verification. Second, it is undeniable that there may be some variables that are not included in the model due to a lack of data, and these variables may have an impact on in-hospital mortality in patients with nonhip femoral fractures.

Conclusion

Our study found that HR, temperature, RDW, BUN, GCS score, SAPSII, CCI and cerebrovascular disease were independent risk factors for hospital death in patients with nonhip femoral fractures. A multiple logistic regression model and a nomogram were developed and validated. During clinical practice, this nomogram could help to improve prognostication in patients with nonhip femoral fractures.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

HR:

Heart rate

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

MAP:

Mean arterial pressure

RR:

Respiratory rate

RBC:

Red blood cell

RDW:

Red blood cell distribution width

WBC:

White blood cell

BUN:

Blood urea nitrogen

PT:

Prothrombin time

PTT:

Partial thromboplastin time

CCI:

Charlson comorbidity index

ICU:

Intensive care unit

MIMIC-IV:

Medical Information Mart for Intensive Care IV

LASSO:

Least absolute shrinkage and selection operator

ROC:

Receiver operating characteristic

AUC:

Area under the receiver operating characteristic curve

NRI:

Net reclassification improvement

IDI:

Integrated discrimination improvement

HL test:

Hosmer‒Lemeshow test

DCA:

Decision curve analysis

GCS:

Glasgow coma Scale

SAPSII:

Simplified acute physiology score II

References

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Vardon F, Mrozek S, Geeraerts T, Fourcade O. Accidental hypothermia in severe trauma. Anaesth Crit Care Pain Med. 2016;35(5):355–61.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  21. Wang HE, Callaway CW, Peitzman AB, Tisherman SA. Admission hypothermia and outcome after major trauma. Crit Care Med. 2005;33(6):1296–301.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kellum JA, Romagnani P, Ashuntantang G, Ronco C, Zarbock A, Anders HJ. Acute kidney injury. Nat Rev Dis Primers. 2021;7(1):52.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    PubMed  PubMed Central  Google Scholar 

  34. Mittal SH, Goel D. Mortality in ischemic stroke score: a predictive score of mortality for acute ischemic stroke. Brain Circ. 2017;3(1):29–34.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This work was supported by Science and Technology Program of Guangzhou (No. 202102010107).

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Author notes

  1. Zhibin Xing, Yuxuan Wu and Wenqiang Che have contributed equally to this work.

Authors and Affiliations

  1. The First Affiliated Hospital of Jinan University, Guangzhou, China

    Zhibin Xing, Yiwen Xu, Wu Yuxuan, Xiaochen Fu, Pengfei Shen & Jing Wang

  2. Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China

    Wenqiang Che

  3. Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, China

    Wenqiang Che

Authors
  1. Zhibin Xing
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  2. Yiwen Xu
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  3. Wu Yuxuan
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  4. Xiaochen Fu
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  5. Pengfei Shen
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  6. Wenqiang Che
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  7. Jing Wang
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Contributions

ZX, YX, and YW contributed to conception and design; ZX, YW contributed to data analysis and interpretation; ZX, YX, XF and PS contributed to drafting the manuscript for intellectual content; WC and JW contributed to revision of the manuscript. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Jing Wang.

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Ethics approval and consent to participate

Our data were collected from the MIMIC-IV version 2.0 database of the intensive care medical information market. To protect patients' privacy, all private information in the database repository has been deleted. Therefore, the requirements for informed consent and ethical approval in this study were waived.

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Not applicable.

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The authors declare that they have no competing interests.

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Xing, Z., Xu, Y., Wu, Y. et al. Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures. Eur J Med Res 28, 539 (2023). https://doi.org/10.1186/s40001-023-01515-7

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  • DOI: https://doi.org/10.1186/s40001-023-01515-7

Keywords

European Journal of Medical Research

ISSN: 2047-783X

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