Prediction model for prolonged length of stay in hip fracture patients

doi:10.21203/rs.3.rs-1628874/v2

Download PDF

Research Article

Prediction model for prolonged length of stay in hip fracture patients

https://doi.org/10.21203/rs.3.rs-1628874/v2

This work is licensed under a CC BY 4.0 License

Version 2

posted

You are reading this older preprint version

Read the latest preprint version →

Background

The length of hospital stay in hip fracture patients is closely associated with medical costs, the burden of which is increasing in aging societies. Herein, we developed and validated models for predicting prolonged length of stay in hip fracture patients to support efficient care in these patients.

Methods

This was a retrospective analysis of all patients undergoing hip fracture from January 2021 to December 2021. Univariate and multivariate logistic regression analyses were used to evaluate the association between risk factors and delayed discharge after hip fracture. Finally, the risk factors obtained from the multivariate regression analysis were used to establish the nomogram model. The validation of the nomogram was assessed by the concordance index (C-index), the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curves. the STROBE checklist was used as the reporting guideline.

Results

A total of 1259 patients were included in the present study for evaluation. Multivariate logistic regression analysis demonstrated that CCI, Preoperative waiting time, Anemia, Hypoalbuminemia and Arteriosclerosis of lower limbs were independent risk factors for delayed discharge after hip fracture. The C-index of this model was 0.82 (95% CI, 0.793–0.847). Internal validation proved the nomogram model’s adequacy and accuracy, and the results showed that the predicted value agreed well with the actual values.

Conclusions

Our prediction models may help policymakers in developing strategies for the optimal management of hip fracture patients with a focus on patients at a high risk of prolonged length of stay.

Delayed discharge

Hip fracture

Risk factor

Nomogram

Of all the fracture types in the elderly, hip fracture is considered to be the most serious fracture. Due to the characteristics of poor prognosis, many complications and high mortality, hip fracture in the elderly is regarded as "the last fracture in life"(Parker & Johansen, 2006). Due to the decline of body function, the decrease of coordination and response ability, the decline of muscle strength around the hip joint, and the increase of age, the calcium loss in the body is serious, and the bone mineral density decreases. Fracture can occur when the hip joint is slightly impacted by external force. In recent years, with the acceleration of the aging process of the population, the elderly are gradually increasing, accordingly, the number of people with hip fractures is increasing year by year. According to statistics, the number of hip fractures worldwide is expected to increase from 1.26 million in 1990 to 4.5 million in 2050, about half of which may occur in Asia, especially in China(Veronese & Maggi, 2018). Hip fractures account for only 14% of all osteoporotic fractures, but require 72% of the total cost, and are expected to cost more than $18.2 billion in the United States by 2025(Burgeet al, 2007).

Studies have shown that hip fracture is the fracture that has the most serious impact on the elderly, which has the characteristics of poor prognosis, many complications and high mortality, which leads to prolonged hospitalization and serious burden on patients and their families(Cauleyet al, 2014; Lealet al, 2016; Yoo, 2019). In addition, the prolongation of hospital stay will also increase the postoperative infection rate of patients(Burgerset al, 2014). According to the study, factors that may affect the LOS of hospitalized patients after hip fracture include osteoarthritis, women, severe postoperative pain, history of stroke, cognitive impairment, comorbid depression, postoperative weight-bearing limitation, and concomitant multiple fractures(Dalyet al, 2020). However, the current literature on the risk factors of delayed hospitalization of hip fractures varies in quality due to different locations, there is a certain degree of uncertainty, and the risk of delayed hospitalization can not be quantified.

Therefore, in order to better understand the factors affecting the length of stay of patients with hip fracture, shorten the length of stay, reduce the cost of hospitalization, and make more efficient use of medical resources, the independent risk factors related to the length of stay of patients with hip fracture were obtained by using logistics regression analysis, and the nomogram model was established. Through the scientific and systematic evaluation of the probability of prolonged hospitalization, the application of nomogram model before operation can more accurately guide clinicians to intervene and treat patients in advance and reduce the occurrence of delayed discharge.

Patients

In total, 1259 patients who underwent treatment for hip fracture between January 2021 and December 2021 were enrolled, and the ethics committee approved the trial at our hospital. The inclusion criteria were as follows: (1) patients 65 years or older. (2) patients undergoing artificial joint replacement or intramedullary nail fixation for hip fracture. The exclusion criteria were as follows: (1)Patients who leave hospital quickly due to non-disease reasons,(2)Pathological fracture, (3)Death in hospital. Of these, 46 patients were excluded: 15 were operated with unknown type, and 31 left the hospital voluntarily.

Ultimately, 1259 patients were included in the study, and they were randomly divided into a training set (70%) and a testing set (30%). Patients in the training set were used to develop the nomogram model, whereas patients in the testing set were used to validate the resulting nomogram.

Data collection

In this study, the factors influencing the delay of discharge were collected through the electronic medical record system. All factors were included Gender, age, hospitalization time, preoperative waiting time, operation type, injury type, fracture type, CCI, multiple injury, hypertension, coronary artery disease, diabetes, cerebrovascular disease, hemiplegia, myocardial infarction, heart failure, arrhythmia, chronic lung disease, connective tissue disease, renal insufficiency, liver insufficiency, anemia, hypoproteinemia, electrolyte disorder, malignant tumor, Alzheimer's disease, Parkinson's disease, arteriosclerosis of lower limbs, second fracture, delirium, digestive tract ulcer .the types of fracture and complications were diagnosed by bone physicians according to the results of imaging examination or laboratory examination after consultation. The hospitalization time is defined as the time from the day of hospitalization to the day before discharge. The 75th percentile of the patient's hospitalization time is used as the boundary value for the extension of hospitalization time. the patients who stayed in hospital for more than 11 days were included in the delayed discharge group, while other patients were included in the normal group. If there is a missing value in the patient data, take the mean value of this variable to replace it. In addition, two team members complete the work of code entry, database establishment and statistical analysis, etc. If there are differences, a third party will solve them to ensure the accuracy of the data.

Statistical analysis

Data were analyzed using the SPSS 22 software for Windows (IBM Corp., Armonk, NY, USA). First, the risk factors that may affect length of stay were classified. Student’s t test or the Mann-Whitney U test was used to perform delayed discharge and non-delayed discharge group comparisons for quantitative variables. Categorical variables were compared using the chisquare or Fisher’s exact test. Second, all the training set factors were included in the univariate and multivariate logistic regression analyses to exclude unrelated risk factors. The independent risk factors obtained based on the multivariate regression method were used to construct a nomogram model with the “rms” package of R software (version 3.6.1).

Finally, the C-index, the area under the ROC curve (AUC), calibration curve, and DCA were used to evaluate the predictive ability and performance of the risk model. The C-index enables evaluation of the predictive accuracy and discriminative ability of nomograms. The C-index values ranged from 0.5-1.0, with low accuracy (< 0.5), moderate accuracy (0.5–0.7), high accuracy (0.7–0.9), and extreme accuracy (> 0.9). A calibration curve was used to compare the actual risk and predicted risk. The clinical usefulness of the nomogram was estimated by DCA based on the net benefit and threshold probabilities. Statistical tests used p < 0.05 as a significance level.

A total of 1259 patients were included in this study: 881 patients were included in the training set, and 378 patients were included in the testing set. In the training set, the delayed discharge group was compared with the non-delayed discharge group, and the results showed that preoperative waiting time, CCI, multiple injuries, coronary artery disease, cerebrovascular disease, hemiplegia, myocardial infarction, heart failure, arrhythmia,, chronic lung disease, anemia, hypoproteinemia, electrolyte disorder, Arteriosclerosis of lower limbs, second fracture, delirium were correlated with delayed discharge. (Table 1). Risk factors (Preoperative waiting time, CCI, anemia, hypoproteinemia, Arteriosclerosis of lower limbs) for prolonged length of stay were obtained by multivariate regression in the training set (Table 2). A new nomogram was constructed to evaluate the delayed discharge probability after hip fracture (Fig. 1). To apply the nomogram model, the scores of different variables are first obtained on the vertical line on the nomogram. Then, the scores of all variables are added to obtain the total score, which finally allows determination of the corresponding predicted risk value by connecting the prediction line to the total score line at the bottom of the nomogram.

The accuracy of the nomogram model was estimated by internal validation in the training set and testing sets. This model’s C-index was 0.82(95% CI,0.793 ~ 0.847), which indicated that the model was predictive with high accuracy. Furthermore, the ROC curve was constructed, and the AUC was calculated for both the training and testing sets. The AUC was 0.82 in the training set (Fig. 2a) and 0.817 in the testing set (Fig. 2b), illustrating that the model had high discrimination. The calibration curves demonstrated good consistency between the model’s actually observed probability and the predicted probability (Fig. 3). DCA indicated that this nomogram model could be an excellent prediction tool for prolonged length of stay after hip fracture (Fig. 4).

Table 1

demographic characteristics in the training set
Characteristics	Delayed discharge,N (%)	Non-delayed discharge,N (%)	t/z/χ²	P
Age			5.657	0.059
65–80	134(56.5)	418(64.9)
81–90	89(37.6)	201(31.2)
> 90	14(5.9)	25(3.9)
Preoperative waiting time			117.207	0.000
≤ 48h	18(7.6)	304(47.2)
> 48h	219(92.4)	430(52.8)
Gender			0.895	0.344
Female	168(70.9)	435(67.5)
Male	69(29.1)	209(32.5)
Type of operation			0.207	0.649
Hip arthroplasty	131(55.3)	367(57.0)
Closed reduction and internal fixation	106(44.7)	277(43.0)
Injury types			1.561	0.212
Low-energy injury	225(94.9)	623(96.7)
High-energy injury	12(5.1)	21(3.3)
Fracture types			4.484	0.106
Femoral neck fracture	134(56.5)	395(61.3)
Intertrochanteric fracture	99(41.8)	246(38.2)
Subtrochanteric fracture	4(1.7)	3(0.5)
CCI			82.579	0.000
≤ 3	170(71.7)	606(94.1)
༞4	67(28.3)	38(5.9)
Multiple trauma			7.596	0.006
No	197(83.1)	579(89.9)
Yes	40(16.9)	65(10.1)
high blood pressure			1.920	0.166
No	102(43.0)	311(48.3)
Yes	135(57.0)	333(57.1)
coronary artery disease			36.829	0.000
No	141(59.5)	513(79.7)
Yes	96(40.5)	131(20.3)
Diabetes			2.419	0.120
No	158(66.7)	464(72.0)
Yes	79(33.3)	180(28.0)
Cerebral vascular disease			14.561	0.000
No	200(84.4)	598(92.9)
Yes	37(15.6)	46(7.1)
Hemiplegia			13.086	0.000
No	192(81.0)	580(90.1)
Yes	45(19.0)	64(9.9)
Miocardial infarction			3.035	0.081
No	233(98.3)	642(99.7)
Yes	4(1.7)	2(0.3)
Heart failure			11.079	0.001
No	223(94.1)	633(98.3)
Yes	14(5.9)	11(1.7)
Arrhythmia			6.847	0.009
No	203(85.7)	590(91.6)
Yes	34(14.3)	54(8.4)
Chronic lung disease			26.625	0.000
No	206(86.9)	619(96.1)
Yes	31(13.1)	25(3.9)
Connective tissue disease			2.095	0.148
No	230(97.0)	636(98.8)
Yes	7(3.0)	8(1.2)
Renal insufficiency			2.283	0.131
No	229(96.6)	633(98.3)
Yes	8(3.4)	11(1.7)
Liver insufficiency			2.217	0.136
No	231(97.5)	638(99.1)
Yes	6(2.5)	6(0.9)
Anemia			114.275	0.000
No	150(63.3)	596(92.5)
Yes	87(36.7)	48(7.5)
Hypoproteinemia			115.305	0.000
No	127(53.6)	562(87.3)
Yes	110(46.4)	82(12.7)
Electrolyte disorder			93.081	0.000
No	165(69.6)	605(93.9)
Yes	72(30.4)	39(6.1)
Malignant tumor			6.520	0.011
No	12(6.5)	14(19.5)
Yes	108(113.5)	345(339.5)
Dementia			2.283	0.131
No	229(96.6)	633(98.3)
Yes	8(3.4)	11(1.7)
Parkinson's disease			0.078	0.781
No	234(98.7)	639(99.2)
Yes	3(1.3)	5(0.8)
Arteriosclerosis of lower limbs			66.146	0.000
No	170(71.7)	596(92.5)
Yes	67(28.3)	48(7.5)
Secondary fracture			7.190	0.007
No	231(97.5)	642(99.7)
Yes	6(2.5)	2(0.3)
Delirium			12.917	0.000
No	222(93.7)	633(98.3)
Yes	15(6.3)	11(1.7)

Table 2

Univariate and multivariate logistic analysis of risk factors for prolonged length of stay after hip fracture
	Univariate		Multivariate
	OR (95% CI)	P	OR (95% CI)	P
Preoperative waiting time
48h	Ref.		Ref.
> 48h	10.878(6.566–18.023)	0.000	11.773(6.635,20.889)	0.000
CCI
< 3	Ref.		Ref.
> 4	6.285(4.077,9.689)	0.000	2.734(1.435,5.211)	0.002
Multiple trauma
No	Ref.		Ref.
Yes	1.809(1.182,2.769)	0.006	1.320(0.757,2.302)	1.320
Coronary artery disease
No	Ref.		Ref.
Yes	2.666(1.931,3.681)	0.000	1.053(0.676,1.641)	0.818
Cerebral vascular disease
No	Ref.		Ref.
Yes	2.405(1.516,3.815)	0.000	1.334(0.72,2.471)	0.360
hemiplegia
No	Ref.		Ref.
Yes	2.124(1.403,3.216)	0.000	1.141(0.629,2.068)	0.664
Heart failure
No	Ref.		Ref.
Yes	3.613(1.616,8.075)	0.002	0.915(0.291,2.874)	0.878
Arrhythmia
No	Ref.		Ref.
Yes	1.830(1.158,2.892)	0.01	1.249(0.687,2.272)	0.466
Chronic lung disease
No	Ref.		Ref.
Yes	3.726(2.150,6.458)	0.000	1.082(0.504,2.322)	0.84
anemia
No	Ref.		Ref.
Yes	7.202(4.850,10.693)	0.000	1.920(1.045,3.529)	0.036
Hypoproteinemia
No	Ref.		Ref.
Yes	5.936(4.205,8.38)	0.000	2.753(1.614,4.696)	0.000
Electrolyte disorder
No	Ref.		Ref.
Yes	6.769(4.421,10.365)	0.000	1.788(0.975,3.278)	0.06
Malignant tumor
No	Ref.
Yes	1.370(0.546,3.436)	0.503
Arteriosclerosis of lower limbs
No	Ref.		Ref.
Yes	4.894(3.254,7.359)	0.000	1.775(1.026,3.071)	0.04
Secondary fracture
No	Ref.		Ref.
Yes	8.338(1.671,41.602)	0.01	1.107(0.193)	6.337
Delirium
No	Ref.		Ref.
Yes	3.888(1.760,8.592)	0.001	2.150(0.776,5.956)	0.141

In this study, the construction of hip fracture patients with prolonged hospital stay risk prediction model, whether the model construction or the internal verification of the data have good sensitivity and specificity, indicating that the prediction effect of the model is good and the result is stable (AUC = 0.794). The model can directly reflect the risk factors of delayed hospitalization in the formula, which can not only predict the risk of delay in patients with clinical hip fracture, but also provide a basis for the follow-up medical staff to systematically evaluate the influencing factors of discharge delay. According to this nomogram, nurses can evaluate the hospitalization time of patients with hip fracture in advance, according to their preoperative waiting time, CCI, anemia, hypoproteinemia, Arteriosclerosis of lower limbs and other influencing factors, to speculate that patients may have delayed discharge risk, so as to shorten hospital stay, reduce the occurrence of postoperative complications, promote postoperative rehabilitation, and optimize the rational allocation of medical resources.

Preoperative Waiting Time

Preoperative waiting time greater than 48 hours is an influencing factor for delayed discharge of hip fracture patients. Consistent with the research results of Muhm et al. (Muhmet al, 2016), the waiting time before operation can affect the time of hospitalization. With the increase of patients' bed time before operation, the incidence of complications will increase, such as periprosthetic infection, pneumonia, urinary tract infection, stroke, myocardial infarction and septic shock, etc., and the deterioration of nutritional status will also lead to the decline of physical function, which will eventually lead to the extension of hospitalization time. (Heet al, 2020; Tang & Fu, 2017)。Sexson and Lehner et al (Sexson & Lehner, 1987) found that early surgery can relieve pain and improve mobility, and may reduce the risk of delirium and lung infection. Foreign literature shows that treatment within the first 24–72 hours of fracture can achieve lower mortality and postoperative complications (Gurger, 2019). It has been agreed that hip fracture patients should be operated as soon as possible, preferably within 48 hours after admission (Simunovicet al, 2010). Therefore, it is very important to give priority to the quick evaluation of hip fracture patients, and delay of operation should be avoided. Although it is best to perform early surgery within two days for patients with stable condition at the time of admission, for patients with complicated complications, surgery should be performed after preoperative optimization. Besides, besides the early operation emphasized in the guidelines, we also advocate early admission. Therefore, we suggest that health administrators and clinicians should strengthen the education and publicity of hip fracture, raise the awareness of the whole society on the severity of such severe cases, improve the timely admission rate, and finally improve the survival rate of hip fracture groups.

CCI

In this study, it was found that the hospitalization delay of patients with hip fracture with CCI ≥ 8 increased by 3.326 times. The CCI score includes the patient's age and various complications, reflecting the patient's overall health at the time of admission. Similar to the conclusions of this study, wei(Weiet al, 2019) found that the co-disease weight of elderly patients with hip fracture (based on age-adjusted CCI assessment) was associated with increased length of stay. A meta analysis also showed that there was a positive correlation between comorbid disease and length of stay of patients with hip fracture(Olthofet al, 2014). It is possible that patients with severe comorbidities may be more prone to complications and require multiple medication, so they will be given longer hospital stay for treatment and care(Charlesworthet al, 2015; Wonget al, 2022). In addition, these patients with a variety of chronic diseases lead to poor self-care ability, but also need a longer recovery period to return to their original self-care level.

Anemia

anemia is closely related to prolonged hospitalization in patients with hip fractures. Nissenholtz et al.(Nissenholtzet al, 2020)found that anemia has a significant effect on the length of stay of elderly patients with hip fracture. On the one hand, because anemic patients need to supplement blood volume and give allogeneic blood transfusion after operation, allogeneic blood transfusion will cause a variety of adverse reactions, affect the patient's immune system and blood coagulation system, and increase the incidence of complications (张 & 张, 2020) .On the other hand, the degree of postoperative anemia in patients with preoperative anemia of hip fracture is relatively severe, which affects the transport of nutrients in the fracture site, weakening the body's defense barrier and hindering the ability of self-repair, resulting in the invasion of pathogenic microorganisms. the final manifestation is an increase in the incidence of complications(Sequeiraet al, 2021). uncorrected anemia after hip fracture may hinder the functional recovery of patients with hip fracture and affect medical complications and length of stay (Sinclairet al, 2021) .

Hypoalbuminemia

Hypoalbuminemia is related to osteoporosis, fractures and falls, which slows down the early postoperative recovery of patients with fractures, increases the incidence of postoperative complications, prolongs the hospital stay, and causes a serious economic burden to patients' families. Hypoalbuminemia is closely related to postoperative hematoma formation, renal insufficiency and cardiovascular adverse events. It also directly affects wound healing by affecting fibroblast proliferation and collagen synthesis(Crosset al, 2014; Huanget al, 2013).Moreover, hypoproteinemia will lead to the decrease of patients' immune function and increase the incidence of wound infection and postoperative pneumonia(Rudasillet al, 2018). BOHL et al (Bohlet al, 2016) showed that preoperative hypoproteinemia can significantly prolong the hospital stay and increase the postoperative readmission rate by 40%. Similar studies have shown that preoperative hypoproteinemia can significantly increase the cost of total hip replacement patients and prolong the length of hospital stay(Rudasillet al, 2018).Therefore, monitoring the serum albumin level to find hypoalbuminemia in the early postoperative period and seeking an optimal scheme of nutritional supplement become a new way to solve the delay of hospitalization and optimize the operation quality of hip fracture patients.

Arteriosclerosis Of Lower Limbs

This study found that preoperative arteriosclerosis obliterans of lower limbs were significantly related to delayed discharge of hip fracture. Arteriosclerosis obliterans of lower limbs is characterized by thickening, calcification, thrombosis, etc. It is a chronic ischemic disease that leads to arterial stenosis or even occlusion. Patients with severe illness are characterized by persistent and severe pain, ulcer and necrosis of the affected limb or toe, which may lead to secondary infection and systemic toxemia. In addition, lower limb ASO will increase the prevalence of cardiovascular and cerebrovascular diseases. Foreign studies show that 40% of lower limb ASO patients suffer from coronary artery or cerebrovascular diseases, and 8.18% of patients suffer from three diseases at the same time (CAPRIE Steering Committee, 1996; Sartipyet al, 2018).Patients with hip fracture may be prolonged in hospital due to the above reasons.

Limitations: this study is a retrospective, single-center study, some indicators are not available; the inclusion of disease heterogeneity, underlying diseases, different etiology of primary diseases, these confounding factors to establish predictive models. The effectiveness of the prediction model still needs to be further verified by multicenter, prospective studies.

In this study of Chinese hip fracture patients, we used management data including clinical information to develop and vali-date risk-adjusted models to predict long-term LOS. Our model and line chart show that high ACCI, bone traction, refracture, anemia, venous thrombosis, pneumonia, and pulmonary infection are the determinants of long-term LOS. Our prediction model can support decision makers in planning strategies for the best management of hip fractures.

Ethics approval

Written informed consent was obtained from the patient for publication of this case report and any accompanying images.

Competing Interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Author contributions

Jian Yu contributed to the work design, draft the article, revise important intellectual content of the article, and the final approval of the version to be submitted.

Hong Cao contributed to the work design, analysis, draft the article, and the final approval of the version to be submitted.

XueJiao Li contributed to the analysis of the work, draft the article, and the final approval of the version to be submitted.

HongYu Guo，YaRu Chang and Yue Li contributed to the analysis of the work, writing of the article, and final approval of the version to be published.

Funding

Not applicable

Availability of data and materials

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

Zhang Li, Zhang Chunlei. (2020). Effect of perioperative allogeneic blood transfusion on coagulation function and immune function of patients undergoing major orthopedic surgery. modern medicine journal of china, 22 (12), 49–52.
Bohl, D. D., Shen, M. R., Kayupov, E., & Della Valle, C. J. (2016). Hypoalbuminemia Independently Predicts Surgical Site Infection, Pneumonia, Length of Stay, and Readmission After Total Joint Arthroplasty. The Journal of Arthroplasty, 31(1), 15–21. https://doi.org/10.1016/j.arth.2015.08.028
Burge, R., Dawson-Hughes, B., Solomon, D. H., Wong, J. B., King, A., & Tosteson, A. (2007). Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. Journal of Bone and Mineral Research: The Official Journal of the American Society for Bone and Mineral Research, 22(3), 465–475. https://doi.org/10.1359/jbmr.061113
Burgers, P. T. P. W., Van Lieshout, E. M. M., Verhelst, J., Dawson, I., & de Rijcke, P. A. R. (2014). Implementing a clinical pathway for hip fractures; effects on hospital length of stay and complication rates in five hundred and twenty six patients. International Orthopaedics, 38(5), 1045–1050. https://doi.org/10.1007/s00264-013-2218-5
CAPRIE Steering Committee. (1996). A randomised, blinded, trial of clopidogrel versus aspirin in patients at risk of ischaemic events (CAPRIE). CAPRIE Steering Committee. Lancet (London, England), 348(9038), 1329–1339. https://doi.org/10.1016/s0140-6736(96)09457-3
Cauley, J. A., Chalhoub, D., Kassem, A. M., & Fuleihan, G. E.-H. (2014). Geographic and ethnic disparities in osteoporotic fractures. Nature Reviews. Endocrinology, 10(6), 338–351. https://doi.org/10.1038/nrendo.2014.51
Charlesworth, C. J., Smit, E., Lee, D. S. H., Alramadhan, F., & Odden, M. C. (2015). Polypharmacy Among Adults Aged 65 Years and Older in the United States: 1988–2010. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 70(8), 989–995. https://doi.org/10.1093/gerona/glv013
Cross, M. B., Yi, P. H., Thomas, C. F., Garcia, J., & Della Valle, C. J. (2014). Evaluation of malnutrition in orthopaedic surgery. The Journal of the American Academy of Orthopaedic Surgeons, 22(3), 193–199. https://doi.org/10.5435/JAAOS-22-03-193
Daly, N., Fortin, C., Jaglal, S., & MacDonald, S. L. (2020). Predictors of Exceeding Target Inpatient Rehabilitation Length of Stay After Hip Fracture. American Journal of Physical Medicine & Rehabilitation, 99(7), 630–635. https://doi.org/10.1097/PHM.0000000000001386
Gurger, M. (2019). Factors impacting 1-year mortality after hip fractures in elderly patients: A retrospective clinical study. Nigerian Journal of Clinical Practice, 22(5), 648–651. https://doi.org/10.4103/njcp.njcp_327_18
He, W., You, Y., Sun, K., Xie, C., Ming, Y., Yu, L., Zhang, F., & Yan, M. (2020). Admission delay is associated with worse surgical outcomes for elderly hip fracture patients: A retrospective observational study. World Journal of Emergency Medicine, 11(1), 27. https://doi.org/10.5847/wjem.j.1920-8642.2020.01.004
Huang, R., Greenky, M., Kerr, G. J., Austin, M. S., & Parvizi, J. (2013). The Effect of Malnutrition on Patients Undergoing Elective Joint Arthroplasty. The Journal of Arthroplasty, 28(8), 21–24. https://doi.org/10.1016/j.arth.2013.05.038
Leal, J., Gray, A. M., Prieto-Alhambra, D., Arden, N. K., Cooper, C., Javaid, M. K., Judge, A., & REFReSH study group. (2016). Impact of hip fracture on hospital care costs: A population-based study. Osteoporosis International: A Journal Established as Result of Cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 27(2), 549–558. https://doi.org/10.1007/s00198-015-3277-9
Muhm, M., Walendowski, M., Danko, T., Weiss, C., Ruffing, T., & Winkler, H. (2016). [Length of hospital stay for patients with proximal femoral fractures: Influencing factors]. Der Unfallchirurg, 119(7), 560–569. https://doi.org/10.1007/s00113-014-2649-5
Nissenholtz, A., Levy, Y., Cooper, L., Bugaevsky, Y., Weiss, A., & Beloosesky, Y. (2020). [ANEMIA IN PATIENTS AFTER HIP FRACTURE REPAIR SURGERY]. Harefuah, 159(9), 689–693.
Olthof, M., Stevens, M., Bulstra, S. K., & van den Akker-Scheek, I. (2014). The association between comorbidity and length of hospital stay and costs in total hip arthroplasty patients: A systematic review. The Journal of Arthroplasty, 29(5), 1009–1014. https://doi.org/10.1016/j.arth.2013.10.008
Parker, M., & Johansen, A. (2006). Hip fracture. BMJ (Clinical Research Ed.), 333(7557), 27–30. https://doi.org/10.1136/bmj.333.7557.27
Rudasill, S. E., Ng, A., & Kamath, A. F. (2018). Preoperative Serum Albumin Levels Predict Treatment Cost in Total Hip and Knee Arthroplasty. Clinics in Orthopedic Surgery, 10(4), 398–406. https://doi.org/10.4055/cios.2018.10.4.398
Sartipy, F., Sigvant, B., Lundin, F., & Wahlberg, E. (2018). Ten Year Mortality in Different Peripheral Arterial Disease Stages: A Population Based Observational Study on Outcome. European Journal of Vascular and Endovascular Surgery: The Official Journal of the European Society for Vascular Surgery, 55(4), 529–536. https://doi.org/10.1016/j.ejvs.2018.01.019
Sequeira, S. B., Quinlan, N. D., Althoff, A. D., & Werner, B. C. (2021). Iron Deficiency Anemia is Associated with Increased Early Postoperative Surgical and Medical Complications Following Total Hip Arthroplasty. The Journal of Arthroplasty, 36(3), 1023–1028. https://doi.org/10.1016/j.arth.2020.09.043
Sexson, S. B., & Lehner, J. T. (1987). Factors affecting hip fracture mortality. Journal of Orthopaedic Trauma, 1(4), 298–305. https://doi.org/10.1097/00005131-198701040-00005
Simunovic, N., Devereaux, P. J., Sprague, S., Guyatt, G. H., Schemitsch, E., Debeer, J., & Bhandari, M. (2010). Effect of early surgery after hip fracture on mortality and complications: Systematic review and meta-analysis. CMAJ: Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne, 182(15), 1609–1616. https://doi.org/10.1503/cmaj.092220
Sinclair, R. C. F., Moppett, I. K., & Gillies, M. A. (2021). Patient blood management and hip fracture. Anaesthesia, 76(3), 417–418. https://doi.org/10.1111/anae.15319
Tang, C., & Fu, S.-P. (2017). [Effect of surgical timing of femoral intertrochanteric fractures on 1-year postoperative mortality in elderly]. Zhongguo Gu Shang = China Journal of Orthopaedics and Traumatology, 30(7), 602–606. https://doi.org/10.3969/j.issn.1003-0034.2017.07.004
Veronese, N., & Maggi, S. (2018). Epidemiology and social costs of hip fracture. Injury, 49(8), 1458–1460. https://doi.org/10.1016/j.injury.2018.04.015
Wei, J., Zeng, L., Li, S., Luo, F., Xiang, Z., & Ding, Q. (2019). Relationship between comorbidities and treatment decision-making in elderly hip fracture patients. Aging Clinical and Experimental Research, 31(12), 1735–1741. https://doi.org/10.1007/s40520-019-01134-5
Wong, R. M. Y., Zu, Y., Chau, W. W., Tso, C. Y., Liu, W. H., Ng, R. W. K., Chow, S. K. H., Cheung, W. H., Tang, N., & Ho, K. K. W. (2022). High Charlson Comorbidity Index Score is associated with early fracture-related complication for internal fixation of neck of femur fractures. Scientific Reports, 12(1), 4749. https://doi.org/10.1038/s41598-022-08855-0
Yoo, J. (2019). Length of hospital stay after hip fracture surgery and 1-year mortality. Osteoporos Int, 9.

No competing interests reported.

Download PDF

Version 2

posted

You are reading this older preprint version

Read the latest preprint version →

Prediction model for prolonged length of stay in hip fracture patients

Archived Versions:

Version 2

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Results

Discussion

Preoperative Waiting Time

CCI

Anemia

Hypoalbuminemia

Arteriosclerosis Of Lower Limbs

Conclusions

Declarations

References

Additional Declarations

Archived Versions:

Version 2

Version 1