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Abstract

Objectives:

Pneumonia is a common perioperative complication in geriatric patients with hip fractures. This study aimed to analyze demographic characteristics, mortality rates, postoperative outcomes, and perioperative comorbidities, identify risk factors for mortality, and develop a nomogram for predicting the prognosis of these patients.

Methods:

Data on patients hospitalized for arthroplasty for hip fractures from 2020 to 2023 at three hospitals were retrospectively analyzed. Patients were divided into the P group (patients with hip fractures complicated with pneumonia) and the C group (patients with hip fractures without pneumonia) and demographic characteristics, mortality, postoperative outcomes, and perioperative comorbidities of the patients were analyzed. Multiple logistic regression was then used to identify independent risk factors for inpatient mortality in the P group and a nomogram was constructed to predict inpatient mortality. The predictive performance of the nomogram was assessed using receiver operating characteristic curves, decision curve analysis, and calibration curves.

Results:

A total of 311 patients participated in the study. Patients in the P group had longer hospitalization (p = 0.001), higher inpatient mortality (p < 0.001), higher mortality (30 days) (p < 0.001), and a poorer recovery of hip function (p < 0.001). Multiple logistic regression showed that age, BMI, total hip arthroplasty, diabetes, and chronic obstructive pulmonary disease were independent risk factors for inpatient mortality in the P group; these factors were incorporated in the nomogram. The C-index of the nomogram was 0.868 (95% CI: 0.802–0.933), and the C-index of internal bootstrapping validation was 0.851 (95% CI: 0.793–0.908), indicating the effectiveness of the nomogram in predicting patient prognosis.

Conclusions:

Coinfection with pneumonia adversely affected both recovery of hip function and survival in geriatric patients with hip fractures. Age, BMI, total hip arthroplasty, diabetes, and chronic obstructive pulmonary disease were found to be independent risk factors for mortality in this patient population.

Keywords

Pneumonia nomogram hip fracture mortality risk factor

Introduction

Hip fractures are common in older adults over the age of 65,¹ and they rank among the top 10 causes of disability.² Hip fractures are associated with high mortality rates within 30 days of their occurrence. The majority of hip fractures fall into two categories: intertrochanteric fractures and fractures of the femur neck, both of which have a similar incidence.³ Older adults with hip fractures face a significantly higher risk of cardiovascular, pulmonary, thrombotic, infectious, and hemorrhagic complications compared to the general population of the same age.⁴ These complications can potentially lead to the patient’s death.⁵

Pneumonia is a common orthopedic perioperative comorbidity and postoperative complication, and its symptoms typically include cough, fever, and respiratory impairment.^6,7 It has been shown that older patients with both pneumonia and hip fractures have higher mortality rates and worse prognoses.^8,9 However, the reasons for these high mortality rates remain unknown.

Therefore, it is important to explore the specific characteristics of these patients and to predict the risk factors that contribute to their mortality. Here we conducted a retrospective analysis of older patients with hip fractures to investigate the statistical associations. This may facilitate a more accurate assessment of older patients with both hip fractures and pneumonia, thereby guiding clinicians in their diagnostic and therapeutic decision-making processes.

Methods

Study design

This retrospective study included patients requiring inpatient surgery for hip fractures from 2020 to 2023, using data from authors’ affiliated institutions. The hospitals involved in this study have extensive competence and experience in the surgical management of hip fractures. The inclusion criteria for the study were (1) Age > 65; (2) Confirmed hip fracture with an indication for surgery; and (3) Agree to complete the follow-up process. The exclusion criteria were (1) Age < 65 years; (2) Periprosthetic hip fracture; (3) Unable to comply with follow-up; and (4) Pathological fractures.

Data collection

With reference to previous publications on pneumonia, the data collected at baseline included the type of fracture and surgical procedure performed, type of anesthesia, perioperative comorbidities, 3-month postoperative Harris hip score (HHS),¹⁰ and 30-day postoperative mortality. Patients were followed up by telephone at 30 days and 3 months post-operatively and information on mortality and functional indicators (HHS score) was recorded.

Quantitative variables

Patients were divided into the P group (patients with hip fractures complicated by pneumonia) and the C group (patients with hip fractures without pneumonia) based on their chest X-rays. Mortality, functional recovery, and perioperative comorbidities were compared between the two groups. To internally validate the model, the 153 patients of the P group were divided into a training and validation cohort at a 7:3 ratio. Variable selection on the training cohort was performed using univariate logistic regression. Variables with p-values below 0.05 were included in the final multiple regression model. A nomogram was constructed using these factors to predict the risk of mortality during hospitalization for patients in the P group. The predictive performance was evaluated utilizing receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves in both training and validation cohorts. The predictive values of the model were assessed according to the C-index. The resampling involved 1000 bootstraps to compute an adjusted C-index for the purpose of internal validation,¹¹ with C-indices closer to one indicating higher prediction accuracy. DCA was used to evaluate the external consistency of the prediction models. A p-value < 0.05 was considered statistically significant.

Statistical analysis

Sample distributions were tested for normality using the Kolmogorov–Smirnov and Anderson–Darling tests. Non-normally distributed variables were compared using the nonparametric rank-sum test. Dichotomous variables are expressed as frequencies and percentages and were compared using Chi-square tests based on minimum frequencies. Multiple logistic regression analysis was used to determine the relationship between multiple variables and outcome indicators. All statistical tests were performed at a level of alpha = 0.05, and 95% confidence intervals were determined. Data were analyzed using SPSS 22.0 (IBM Corp., Armonk, NY, USA). R Studio (V4.3.2) was used for nomogram construction, ROC curves, DCA, and calibration curve plotting. This study utilized the TRIPOD checklist for standardization purposes.¹²

Results

The study initially recruited 476 patients, of whom 165 were screened out based on the exclusion criteria, and the remaining 311 patients with hip fractures were finally enrolled. There were 153 patients in group P and 158 in group C (Figure 1). There were no significant differences between the two groups in terms of sex (p = 0.855), age (p = 0.808), BMI (p = 0.069), fracture type (p = 0.494), and fracture side (p = 0.588). Patients in the P group had a higher prevalence of smoking compared to those in the C group (p = 0.016) (Table 1).

Figure 1.

The flow diagram of this study.

Table 1.

Demographic and clinical characteristics of elderly hip fracture patients.

Demographics	P group (n = 153)	C group (n = 158)	p
Gender, n (%)
Male	72 (47)	77 (49)	0.855
Female	81 (53)	81 (51)	0.855
Age, n (%)
<75	57 (37)	62 (39)	0.808
>75	96 (63)	96 (61)	0.808
BMI, n (%)
18.5–27.9	114 (75)	132 (84)	0.069
<18.5 OR >2828.0	39 (25)	26 (16)	0.069
Smoking, n (%)
No	124 (81)	144 (91)	0.016
Yes	29 (19)	14 (9)	0.016
Fracture type, n (%)
Femoral neck fracture	96 (63)	106 (67)	0.494
Intertrochanteric fracture	57 (37)	52 (33)	0.494
Fracture side, n (%)
Left	66 (43)	74 (47)	0.588
Right	87 (57)	84 (53)	0.588
Surgery type, n (%)
HA	69 (45)	66 (42)	0.633
THA	84 (55)	92 (58)	0.633
Anesthesia type, n (%)
GA	86 (56)	90 (57)	0.984
CSEA	67 (44)	68 (43)	0.984
Length of stay (LOS), median (Q1, Q3)	12 (10, 20)	11 (8, 15.5)	0.001
Time to surgery, median (Q1, Q3)	7 (4, 10)	5 (3, 6.75)	<0.001
CHD, n (%)
Negative	122 (80)	140 (89)	0.047 (0.362–2.655)
Positive	31 (20)	18 (11)	0.047 (0.362–2.655)
Hypertension, n (%)
Negative	89 (58)	90 (57)	0.920 (0.404–2.053)
Positive	64 (42)	68 (43)	0.920 (0.404–2.053)
CVD, n (%)
Negative	150 (98)	154 (97)	1.000 (0.183–23.802)
Positive	3 (2)	4 (3)	1.000 (0.183–23.802)
Diabetes, n (%)
Negative	109 (71)	118 (75)	0.578 (2.009–10.758)
Positive	44 (29)	40 (25)	0.578 (2.009–10.758)
COPD, n (%)
Negative	88 (58)	136 (86)	<0.001 (2.101–12.459)
Positive	65 (42)	22 (14)	<0.001 (2.101–12.459)
Renal insufficiency, n (%)
Negative	96 (63)	126 (80)	0.001 (1.017–5.135)
Positive	57 (37)	32 (20)	0.001 (1.017–5.135)
Hepatic insufficiency, n (%)
Negative	137 (90)	146 (92)	0.494 (0.425–4.770)
Positive	16 (10)	12 (8)	0.494 (0.425–4.770)
Anemia, n (%)
Negative	69 (45)	66 (42)	0.633 (0.352–1.741)
Positive	84 (55)	92 (58)	0.633 (0.352–1.741)
VTE, n (%)
Negative	129 (84)	126 (80)	0.368 (0.150–1.945)
Positive	24 (16)	32 (20)	0.368 (0.150–1.945)
Inpatient mortality, n (%)
Live	82 (80)	154 (97)	<0.001
Dead	30 (20)	4 (3)	<0.001
Mortality (30 days), n (%)
Live	120 (78)	150 (95)	<0.001
Dead	33 (22)	8 (5)	<0.001
Harris hip score (3 months), n (%)
⩾90	39 (33)	48 (32)	<0.001
80–89	39 (33)	72 (48)
70–79	36 (30)	28 (19)
<70	6 (4)	2 (1)

P group: patients with pneumonia; C group: patients without pneumonia; BMI: body mass index; Q1: 25% quartile; Q3: 75% quartile; THA: total hip arthroplasty; HA: hemiarthroplasty; GA: general anesthesia; CSEA: complicated spinal epidural anesthesia; CHD: chronic heart diseases; COPD: chronic obstructive pulmonary disease; VTE: venous thrombosis; CVD: cerebrovascular disease.

Bold numbers represent values less than 0.05, and the difference is statistically significant.

Differences in surgery and anesthesia types were then assessed between the groups, using the parameters of total hip arthroplasty (THA), hemiarthroplasty (HA, general anesthesia (GA), and complicated spinal epidural anesthesia (CSEA). The results showed THA/HA: P (45%/55%), C (42%/58%) (p = 0.633); GA/CSEA: P (56%/44%), C (57%/43%) (p = 0.984) indicating that there were no significant differences in either surgery or anesthesia types between the two groups. It was observed that the mean length of stay (LOS) was longer in the P group relative to the C group (12/11, p = 0.001), and the interval between fracture and surgery (7/5, p < 0.001) was also prolonged (Table 1).

The perioperative comorbidities were analyzed in the 311 patients. These included anemia, chronic heart disease (left ventricular ejection fraction < 40%), hypertension, diabetes, chronic obstructive pulmonary disease (COPD), hepatic insufficiency (total bilirubin > 34 μmol/L or albumin < 35 g/L), renal insufficiency (estimated glomerular filtration rate < 60 mL/min/1.73 m²), venous thrombosis, and cerebrovascular disease. Chronic heart diseases (P: 20%, C: 11%, p = 0.047), COPD (P: 42%, C: 14%, p < 0.001), and renal insufficiency (P: 37%, C: 20%, p = 0.001) were more prevalent in the P group relative to the C group (Table 1). Analysis of inpatient mortality and 30-day postoperative mortality showed that mortality was greater in the P group than in the C group, with inpatient mortality (P: 20%, C: 3%, p < 0.001) and 30-day postoperative mortality (P: 22%, C: 5%, p < 0.001). The HHS scores were also analyzed 3 months after surgery, showing that these were markedly lower in the P group compared to the C group (p < 0.001) (Table 1).

We then explored the risk factors affecting mortality in the P-group patients. We divided the 153 patients in Group P into training cohort (107 patients) and validation cohort (46 patients) according to a 7:3 ratio, and conducted binary univariate logistic regression analysis on the patients in the training set to explore their mortality-related risk factors; the demographic and clinical characteristics of the patients are shown in Table 2. The results of the analysis identified 5 potential predictive factors of the original 17 factors analyzed, each exhibiting p-values below the threshold of 0.05 (Table 2). Subsequent multiple logistic regression analysis of these five predictors indicated that age (p = 0.018, OR = 0.246, 95% CI: 0.077–0.786), BMI (p = 0.002, OR = 0.199, 95% CI: 0.072–0.550), surgery type (p = 0.044, OR = 0.345, 95% CI: 0.122–0.973), diabetes (p < 0.001, OR = 7.020, 95% CI: 1.397–21.553), and COPD (p < 0.001, OR = 10.349, 95% CI: 2.500–39.398) were independent risk factors for inpatient mortality in geriatric patients with hip fractures and pneumonia (Table 3).

Table 2.

Demographic and clinical characteristics of training cohort in P-group patients and results of univariable regression analysis.

Demographics	Surviving (n = 82)	Dead (n = 25)	p-value	OR (95% CI)
Gender, n (%)
Male	40 (49)	11 (44)	0.675	1.212 (0.493–2.983)
Female	42 (51)	14 (56)	0.675	1.212 (0.493–2.983)
Age, n (%)
<75	58 (71)	12 (48)	0.040	2.618 (1.046–6.553)
>75	24 (29)	13 (52)	0.040	2.618 (1.046–6.553)
BMI, n (%)
18.5–27.9	65 (79)	13 (52)	0.009	3.529 (1.366–9.117)
<18.5 OR >28.0	17 (21)	12 (48)	0.009	3.529 (1.366–9.117)
Smoking, n (%)
Negative	71 (87)	20 (80)	0.422	1.614 (0.502–5.188)
Positive	11 (13)	5 (20)	0.422	1.614 (0.502–5.188)
Fracture side, n (%)
Left	36 (46)	7 (41)	0.755	1.154 (0.469–2.841)
Right	43 (54)	10 (59)	0.755	1.154 (0.469–2.841)
Fracture type, n (%)
Femoral neck fracture	53 (65)	15 (60)	0.674	1.218 (0.486–3.055)
Intertrochanteric fracture	29 (35)	10 (40)	0.674	1.218 (0.486–3.055)
Surgery type, n (%)
HA	52 (63)	6 (24)	0.001	5.489 (1.975–15.251)
THA	30 (37)	19 (76)	0.001	5.489 (1.975–15.251)
Anesthesia type, n (%)
GA	26 (32)	13 (52)	0.069	0.429 (0.172–1.067)
CSEA	56 (68)	12 (58)	0.069	0.429 (0.172–1.067)
CHD, n (%)
Negative	75 (91)	20 (80)	0.122	2.679 (0.768–9.341)
Positive	7 (9)	5 (20)	0.122	2.679 (0.768–9.341)
Hypertension, n (%)
Negative	59 (72)	13 (52)	0.067	2.368 (0.943–5.946)
Positive	23 (28)	12 (48)	0.067	2.368 (0.943–5.946)
CVD, n (%)
Negative	81 (98)	24 (96)	0.396	3.375 (0.203–56.002)
Positive	1 (2)	1 (4)	0.396	3.375 (0.203–56.002)
Diabetes, n (%)
Negative	69 (84)	13 (52)	0.002	4.899 (1.833–13.097)
Positive	13 (16)	12 (48)	0.002	4.899 (1.833–13.097)
COPD, n (%)
Negative	57 (70)	7 (28)	<0.001	5.863 (2.175–15.803)
Positive	25 (30)	18 (72)	<0.001	5.863 (2.175–15.803)
Renal insufficiency, n (%)
Negative	59 (72)	14 (56)	0.138	2.016 (0.799–5.083)
Positive	23 (28)	11 (44)	0.138	2.016 (0.799–5.083)
Hepatic insufficiency, n (%)
Negative	76 (93)	22 (88)	0.465	1.727 (0.399–7.474)
Positive	6 (7)	3 (12)	0.465	1.727 (0.399–7.474)
Anemia, n (%)
Negative	38 (46)	12 (48)	0.884	0.936 (0.382–2.294)
Positive	44 (54)	13 (52)	0.884	0.936 (0.382–2.294)
VTE, n (%)
Negative	68 (83)	23 (92)	0.277	0.422 (0.089–2.000)
Positive	14 (17)	2 (8)	0.277	0.422 (0.089–2.000)

OR: odds ratio; 95% CI: 95% confidence interval.

Bold numbers represent values less than 0.05, and the difference is statistically significant.

Table 3.

The results of multivariate logistic regression analysis.

Demographics	B	SE	Wald	p-value	OR (95% CI)
Age	1.975	0.721	7.503	0.006	7.209 (1.754–29.628)
BMI	1.836	0.590	9.672	0.002	6.273 (1.972–19.956)
Surgery type	−1.368	0.540	6.405	0.011	0.255 (0.088–0.735)
Diabetes	2.371	0.597	15.769	<0.001	10.713 (3.323–34.533)
COPD	1.908	0.577	10.920	0.001	6.742 (2.174–20.909)

OR: odds ratio; 95% CI: 95% confidence interval.

Inpatient mortality in the training cohort was used as the outcome index. A nomogram was constructed using the identified independent risk factors (Figure 2). The model’s validation relied primarily on its discrimination and calibration. The nomogram had good predictive accuracy with an optimism-corrected C-indices of 0.851 (95% CI: 0.793–0.908) and was well-calibrated as shown in Figure 3.

Figure 2.

Nomogram. A nomogram to predict the probability of death in patients with hip fractures complicated pneumonia. Assign a value to each factor, obtain the corresponding value on the horizontal axis, and sum up the values for all factors. Mark the total points on the total axis. The values on the bottom line evaluate the probability of death. Age (0 represents > 75, 1 represents < 75), BMI (0 represents < 18.5 OR >28.0, 1 represents 18.5–27.9), Surgery type (0 represents > 75, 1 represents < 75), COPD (0 represents negative, 1 represents positive), BMI (0 represents negative, 1 represents positive).

Figure 3.

(a) and (d) are the ROC curves of the training and validation. The receiver operating characteristic curve (AUC) of nomogram. (b) and (e) are the calibration curves of the training and validation. The x-axis represents the predicted death, while the y-axis represents the actual probability. The diagonal line represents a perfect prediction. The solid line indicates the performance of the nomogram. The closer the solid line fit to the diagonal line represents, the better the prediction model. (c) and (f) are the decision curves of the training and validation. Decision curve analysis for nomogram prediction of risk of mortality in elderly patients with pneumonia complicated with hip fracture. The y-axis represents the net benefit. The solid lines represent the probability of death. The gray thin solid line represents the assumption that all P-group patients die during hospitalization. The solid black line represents the assumption that no P-group patients died during hospitalization.

Discussion

In our study, we observed longer LOS, as well as the time from fracture to surgery, in the P group. We believe that these differences could be attributed to pneumonia-related comorbidities. Corrales et al.¹³ showed that hospitalized patients with pneumonia had a higher incidence of cardiovascular disease and a higher in-hospital mortality rate. Suter-Widmer et al.¹⁴ also found that older age, heart failure, and COPD in patients with pneumonia were independently predictive of prolonged hospital stay. This would explain the longer hospitalization of geriatric patients with hip fractures complicated with pneumonia.

It was found that patients in the P group had higher inpatient mortality, 30-day postoperative mortality, and significantly worse HHS at 3 months following surgery and that patients with underlying diseases were more likely to experience severe illnesses. The combination of underlying diseases and reduced immune and organ function often leads to a poorer prognosis. At the same time, the increased postoperative bed rest and reduced functional exercise lead to muscle loss.¹⁵ Menéndez et al.¹⁶ found that hospitalized patients had higher mortality rates for comorbid pneumonia, and that pneumonia was an independent factor for death among geriatric hospitalized patients. Chen et al.¹⁷ also found that obese patients with pneumonia, fractures, and low albumin levels were at higher risk of death. However, further research is needed to clarify the reason for the reduced hip function in geriatric patients with hip fractures complicated by pneumonia.

Based on literature reports on pneumonia,¹⁸ we collected information on perioperative comorbidities in the 311 patients included in the study, showing significant differences in the prevalence of chronic heart diseases (CHD), COPD, and renal insufficiency between the two groups. Previous studies have identified several possible exposure factors for pneumonia complicated with hip fracture. Ochoa-Gondar et al.¹⁹ found that the risk factors for streptococcal pneumonia include old age, low immunity, and COPD. Feldman et al.²⁰ found that patients with pneumonia had a higher risk of comorbid cardiac complications during the first 7 days of hospitalization. Patients with comorbid pneumonia had a higher risk of developing acute coronary syndrome compared to normal patients. Patients with pneumonia are more likely to have comorbid cardiovascular disease. On the one hand, they are more likely to have comorbidities such as COPD and chronic kidney disease, as well as a variety of other factors associated with old age, while on the other hand, they may have reduced immune function due to malnutrition. Furthermore, fluid accumulation in the alveoli may contribute to disease, as the alveolar fluid slows the immune response while enhancing localized infection.²¹ This evidence suggests that patients with comorbid pneumonia have a more fragile immune function and a higher risk of cardiac disease and renal impairment, which persisted in the geriatric hip fracture patients with comorbid pneumonia included in our study. Therefore, prevention of these conditions should be strengthened in this group of patients.

Through a multiple logistic regression analysis of the potential predictors and the construction of a nomogram, ROC curves, DCA, and calibration curves, age, BMI, surgery type, diabetes, and COPD were identified as independent risk factors for mortality in geriatric hip fracture patients with pneumonia. Older age is usually associated with more underlying diseases.²² We believe that old age is the key factor for the death of patients in group P. Xie et al.’s²³ study showed that old age, male sex, renal injury, Acute Lung Injury/Acute Respiratory Distress Syndrome (ALI/ARDS), hypotension, and other factors may be risk factors for the death of patients with pneumonia. Diabetes is one of the most common underlying diseases in older adults and one of its pathological features is elevated blood glucose,²⁴ which has been shown in numerous studies to be involved in the glycosylation of complement and immunoglobulins, reducing immune function and thus increasing patient susceptibility to infection.²⁵ Several studies have also suggested that hyperglycemia can trigger oxidative stress in lung tissues, increasing the levels of oxygen and nitrogen radicals and accelerating the progression of pneumonia.^26–28 Our results suggest that immunosuppression induced by hyperglycemia as well as the increased oxidative stress enable faster progression of pneumonia in patients in the P group. In addition, patients in group P were bedridden for a long period, exacerbating pneumonia. At the same time, they had a greater probability of impaired organ function, and the combined effect of these factors may have mediated the fatal outcome. Nutritional status is likewise important in geriatric patients.²⁹ Previous studies have shown that malnutrition is common in geriatric hip fracture patients, with varying degrees of impact on length of hospitalization, prognosis, and mortality.^30,31 Malnutrition can also lead to weakness and sarcopenia, which can have a significant impact on healing and functional recovery.^32,33 On the other hand, obesity is also an important factor that threatens life and health.³⁴ Wang et al.³⁵ have shown that obese patients with hip fractures have a higher risk of major complications and a higher risk of kidney damage. The present results also found that renal insufficiency and high BMI were risk factors for death in group P, which may be due to obesity-mediated disorders of lipid and fatty acid metabolism that exacerbate renal impairment, and obesity may also mediate comorbidities such as coronary heart disease and dementia.^36,37 Our study showed that the odds of death in group P patients who underwent THA was nearly three times higher than that in patients who were treated with HA (OR = 0.345, 95% CI: 0.122–0.973). THA is associated with more surgical trauma, longer postoperative recovery, and bed rest, and is more likely to be associated with surgical comorbidities such as wound infections, pneumonia, and even death.³⁸ Consequently, the preference for HA may have reduced the risk of patient death in the P group, and further studies with larger sample sizes and more comprehensive indicators are warranted.

However, this study has certain limitations. First, the optimal sample size was not calculated which may have resulted in some bias in the results which have affected the generalizability of the results, despite collecting data from multiple medical centers. We did not register the different levels of severity of the comorbidities (such as CHD and renal insufficiency), which may have led to omissions in the statistics of comorbidities in some patients. The small sample size and the low prevalence of inpatient mortality in this study introduce uncertainty to the accuracy and odds ratios we obtained. At the same time, further research with larger sample sizes is needed in the future and external validation of the model is needed to improve its credibility and enable it to guide clinicians in decision-making.

Conclusion

This study investigated geriatric patients with hip fractures complicated with pneumonia. The results showed that coinfection with pneumonia affected both survival and recovery of hip function in these patients. The study also identified risk factors for death in geriatric hip fracture patients complicated with pneumonia, older age, too high or low BMI, THA, diabetes, and COPD were found to be independent risk factors for mortality in this patient population. A nomogram was also constructed to predict the mortality of these patients. This could guide clinicians in the assessment of geriatric patients with hip fractures and pneumonia.

Footnotes

Acknowledgements

We are grateful to the patients for providing the information and cooperating with our follow-up visit. We are also grateful to these three hospitals for providing us with data.

Author contribution

KZ and XL collected the clinical data and wrote the paper. YL, HZ, and FZ contributed to statistical analysis. CZ reviewed and edited the paper. YX designed the study. MN did the critical review and finalized the paper. Written informed consent to publication was obtained.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Kuanren Talents Program of the second affiliated hospital of Chongqing Medical University, the Second Affiliated Hospital of Chongqing Medical University, 2022 COVID-19 Infection Emergency Special Funding Project, and the Program for Youth Innovation in Future Medicine of Chongqing Medical University.

Ethical approval statement

Ethical approval for this study was obtained from the Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University (2023 No. 43, 2023/09/01), The People’s Hospital of Linshui County (LYLL2023073, 2023/09/20) and the People’s Hospital of Fengjie County (2023 No. 76, 2023/09/26).

Informed consent

This study is a retrospective cohort study and written informed consent was exempted by the Institutional Review Board. This study has obtained oral informed consent from patients, who have agreed to collect their data for public publication.

Trial registration

Not applicable: This is a retrospective clinical study and has no registry number.

ORCID iD

Kaiming Zhang

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A nomogram for predicting mortality risk in geriatric patients with hip fractures complicated by pneumonia: A multicenter study