Proximal femur fractures (PFF) are a common injury in elderly patients that significantly impact mobility and daily living activities. Mortality rates in this population are also high, making effective treatment essential. Recent advances in intensive and geriatric care have enabled complex surgical interventions that were previously not feasible. However, there is a lack of studies focusing on outcome parameters in very elderly patients (≥ 90 years) who receive intensive care treatment following PFFs. In this retrospective study, we analyzed multi-layered data of 148 patients who were 90 years or older and received intensive care after trauma and orthopedic surgical treatment for PFFs or periprosthetic fractures between 2009 and 2019. All patients received a 365-day follow-up. To identify potential predictors of mortality, all deceased and surviving patients were subjected to multiple logistic regression analyses. We found that 22% of patients deceased during in-hospital care, and one-year survival was 44%. Independent predictors of one-year all-cause mortality included higher CCI and SOFA scores at ICU admission. Overall, 53% of patients who resided in private dwellings prior to admission were able to return home. Our study highlights the utility of using CCI and SOFA scores at ICU admission as prognostic indicators in critically ill very elderly patients who undergo surgical treatment for PFFs. These scores can provide valuable insight into the severity of illness and potential outcomes, which can inform resource allocation, prioritize endangered patients, and aid in end-of-life discussions and planning with patients and their families. Our findings can help improve the management of PFFs in very elderly patients and contribute to optimized patient care.
Proximal femur fractures (PFFs) are a common occurrence among elderly patients over the age of 75 and represent a detrimental life event that undermines their already compromised independence[
So far, no studies focused on outcome parameters of very elderly patients ≥ 90 years who received intensive care treatment following PFFs. Specifically, the one-year all-cause mortality rate in this patient population has not been studied. This study aims to investigate the clinical characteristics and outcomes, including in-hospital and one-year mortality, of critically ill nonagenarians in a large university hospital with a focus on tertiary care.
In this study, we retrospectively analysed data of patients who were 90 years or older and received trauma and orthopedic surgical treatment for a proximal femur or periprosthetic fracture between 2009 and 2019. Inclusion criteria for the cohort was post-operative admission to the tertiary Department of Intensive Care Medicine (ICU) of the University Medical Centre Hamburg-Eppendorf. The following data were extracted from the electronic patient data management system (Integrated Care Manager [ICM], Dräger Medical, Lübeck, Germany): Age, gender, type of residence before admission and after discharge, comorbidities, surgical details (diagnosis and intervention), ICU treatment (including laboratory results, vital parameters, treatment modalities and organ support, blood transfusions), discharge information, all-cause ICU- and in-hospital mortality.
The simplified acute physiology score II (SAPS II) was used to access severity of illness in all patients[
Comorbidities were analysed using the Charlson Comorbidity Index (CCI)[
At admission and regularly during ICU treatment, laboratory tests, blood gas analysis, vital functions, ventilation parameters, pharmacological circulatory support, transfusions, and neurological status were recorded. Follow-up regarding all-cause mortality and living situation was obtained after discharge at 28-, 90-, and 365-day intervals.
The Ethics Committee of the Hamburg Chamber of Physicians (Ethikkomission der Ärztekammer Hamburg) approved the study (No.: 2021–300,116-WF). Due to the retrospective nature of the study and the deidentified study data, the need for explicit informed consent was waived by the ethics Committee of the Hamburg Chamber of Physicians (Ethikkomission der Ärztekammer Hamburg). The study was conducted in compliance with the Declaration of Helsinki as well as in accordance with local guidelines and regulations.
The study included proximal femur fractures, including femoral neck, pertrochanteric, and subtrochanteric fractures, as well as fractures in the vicinity of pre-existing hip replacements. The diagnosis was established using radiographic or computed tomographic imaging, which were reviewed by a board-certified radiologist and trauma/orthopedic surgeon.
For patients with native joint fractures, treatment consisted of either osteosynthesis (nailing) for per- or subtrochanteric fractures, or hemi-hip-arthroplasty/total hip arthroplasty based on individual lifestyle and health factors for femoral neck fractures (Fig. 1a). In cases of periprosthetic fractures, osteosynthesis, aseptic one-stage implant revision surgery, or a combination of the two was conducted (Fig. 1b). The procedures were performed by board-certified trauma or arthroplasty specialists at our Level 1 trauma center.
Graph: Figure 1Operative intervention in patients presenting with (a) PFFs or (b) periprosthetic femur fracture. (a) First case [
Patients were preoperatively evaluated by a board-certified anaesthesiologist for operative risk factors and health status and scheduled for postoperative critical care if necessary. Whilst no strict protocol is in place for admitting nonagenarians and centenarians to the ICU after surgical treatment for PFF, we take a multidisciplinary approach specific to each patient. Patient will, comorbidities, health status as well as intra- and postoperative parameters (hemodynamic and pulmonary changes) are discussed within the multidisciplinary team of anesthesiologist, intensivist, and treating surgical team before transferring a patient to the ICU or surgical ward after initial post-anesthesia care unit (PACU) monitoring.
In accordance with national treatment guidelines, surgical intervention was performed within 24 h, unless there were contraindications such as anticoagulants or unfavourable health status[
Postoperatively, patients received treatment at one of our 12 specialized ICUs with a total capacity of 142 beds and a high level of care. After sufficient recovery, patients were transferred to our surgical wards. All patients received a daily session of physiotherapy and were transferred preferably to rehabilitation, alternatively to specialized geriatric care facilities for further recovery or their care facilities or homes.
All analyses were performed using SPSS version 29 (IBM, Armonk, New York, NY, USA) for Windows. Continuous variables are expressed as mean ± standard deviation (SD), while categorical variables are expressed as a number and percentage (%). Normality distribution of data was tested using the Shapiro–Wilk test. To compare patients in terms of continuous variables, the Student's t-test for independent samples was used for normally distributed data. The Mann − Whitney U test was used with non-normally distributed data, and chi-squared or Fisher's exact tests were applied for categorial variables. The probability of 365 days survival was estimated as a function of time using the Kaplan–Meier survival method with a 95% confidence interval. To identify potential predictors of mortality, all deceased and surviving patients were subjected to multiple logistic regression analyses. To control prerequisites of the regression, Pearson correlation for continuous and Spearman correlation for nonparametric variables were applied. Correlations among predictors should amount less than 0.7. Both, continuous and categorical variables were offered to the logistic regression model. Predictors were logarithmised (Log base 10) for analysis if they were not normally distributed. The analyses used the 'stepwise backwards logistic regression' method applying the maximum likelihood function to compensate for nonlinearity regarding the influence of continuous variables. The goodness of fit was judged with Nagelkerke's R
During the study period from 2009 – 2019 we could identify 1108 patients ≥ 90 years admitted to the ICU. Of those 13.3% (n = 148) were admitted for trauma and orthopaedic surgical treatment and had proximal femur or periprosthetic fracture. Whitin the timeframe, a total of 246 nonagenarians and centenarians were surgically treated due to a PFF. The rate of postoperative ICU admission was 51% between 2009–2014 and 70% between 2015 – 2019, whilst number treated, and age were 127 versus 119, and 95 versus 94 years for the respective time spans. Patients not admitted to the ICU were moved to the surgical ward after postoperative monitoring at the post-anesthesia care unit (PACU). A total of 148 patients were eligible for retrospective analysis.
The mean age at admission was 94.2 ± 3 years [90 – 106 years], and the majority of patients were female (81%, n = 117). Preoperatively, 41% of patients (n = 61) were residing in a private dwelling, 46% (n = 68) in a long-term care facility, and 14 patients (10%) were living in an assisted living facility. CCI in the study cohort ranged from 0 to 7 points, with an average of 1.5 ± 1.5. 76% (n = 112) of patients had a preoperative ASA score of 3 ± 0.5. Table 1 provides additional demographic and disease characteristics of the patient population.
Table 1 Shown are baseline characteristics of the study population.
Variables All (n = 145) 365 d Survival (n = 64) Deceased before 365 d (n = 81) Age (years) (mean ± SD) 94.1 ± 3.0 93.9 ± 3.1 94.3 ± 3.0 0.38 Female (n, %) 117, 80.7% 53, 82.8% 64, 79.0% 0.67 BMI (kg/m2) (mean ± SD) 22.82 ± 3.87 22.80 ± 3.97 22.84 ± 3.81 0.95 Comorbidities CCI (points) 1.5 ± 1.5 (range, 0–7) 0.9 ± 1.0 1.9 ± 1.6 Arterial hypertension (n, %) 106, 73.1% 46, 71.9% 60, 74.1% 0.85 Chronic kidney disease (n, %) 36, 24.8% 10, 15.6% 26, 32.1% Aortic valvular stenosis (n, %) 9, 6.2% 5, 7.8% 4, 4.9% 0.5 Atrial fibrillation (n, %) 56, 38.6% 24, 37.5% 32, 39.5% 0.86 Congestive heart failure (n, %) 35, 24.1% 10, 15.6% 25, 30.9% Diabetes mellitus (n, %) 12, 8.3% 5, 7.8% 7, 8.6% 1.0 Lung disease (n, %) 8, 5.5% 1, 1.6% 7, 8.6% 0.07 COPD 5, 3.4% 0, 0.0% 5, 6.2% 0.06 Dementia (n, %) 48, 33.1% 14, 21.9% 34, 42.0% PAD (n, %) 8, 5.5% 2, 3.1% 6, 7.4% 0.46 Prevalent cancer (n, %) 8, 5.5% 1, 1.6% 7, 8.6% 0.07
d days, CCI Charlson comorbidity index, COPD chronic respiratory pulmonary obstruction, PAD peripheral artery disease. Bold print indicating significant variations between the two groups.
The most common type of fracture was at the femoral neck (53%, n = 78), followed by fractures of the pertrochanteric or intertrochanteric region (30%, n = 44), periprosthetic fractures (14%, n = 20), and subtrochanteric fractures (3%, n = 5). 52% of patients (n = 77) underwent surgery outside of regular hours (7 p.m. – 8 a.m.) or on weekends. The median surgery time was 80 ± 40 min, and no correlation was found between surgery time of day or duration and mortality.
All patients received postoperative intensive care treatment either due to pre-existing critical illness or an increased risk of perioperative adverse events identified during surgery. On average, patients spent 12 ± 7 days hospitalized and 2.4 ± 3 days in the ICU. At admission to the ICU, the SAPS II scores of the patient cohort ranged from 18 to 67 points (mean of 37.6 ± 11.6), and the SOFA scores ranged from 0 – 12 points (mean of 2.9 ± 3). The CCI ranged from 0—7 (mean 1.5 ± 1.5). Sixteen patients (11%) required invasive ventilation and seven (5%) non-invasive ventilation, with an average duration of less than 24 h.
At admission, the average venous lactate was 1.5 ± 1.2 mg/dl, haemoglobin was 10 ± 1.4 g/L and the INR was 1.1 ± 0.15. Three patients (2%) required postoperative transfusions of red blood cells due to bleeding, but no additional surgical interventions were necessary. On average, 42% of ICU patients (n = 62) received 1.7 ± 0.86 red blood cell transfusions. Seven patients (5%) received transfusions of three or more units. There was no correlation between haemoglobin levels or transfusion rates and mortality. Additional patient information, including laboratory results and treatment details, can be found in Table 2.
Table 2 Shown are laboratory and blood gas analysis results comparing value at admission and last measurement before discharge or patient death.
Variables All Patients with 365-day survival Patients deceased before 365 days Vital functions – at admission Glasgow coma scale 13.4 ± 3.9 14.0 ± 3.2 12.9 ± 4.2 0.07 Body temperature (°C) 35.9 ± 1.0 36.0 ± 0.9 35.8 ± 1.0 0.23 Heart rate (beats/minute) 87.3 ± 25.2 82.1 ± 18.6 91.3 ± 28.8 Mean arterial pressure (mmHg) 86.3 ± 20.4 90.1 ± 19.1 83.3 ± 21.0 Vasopressor use Catecholamine 53, 36.6% 18, 28.1% 35, 43.2% 0.08 Epinephrine 3, 2.1% 0, 0.0% 3, 3.7% 0.25 Norepinephrine 53, 36.6% 18, 28.1% 35, 43.2% 0.08 Respiratory support – admission paO2/FIO2 473.0 ± 328.0 515.3 ± 327.1 437.6 ± 327.2 0.21 Invasive mechanical ventilation 16, 11.0% 4, 6.3% 12, 14.8% 0.11 Non-Invasive ventilation/HFNC 7, 4.8% 4, 6.3% 3, 3.7% 0.70 ICU Outcome Duration of ICU stay (days) 2.4 ± 3.3 2.14 ± 3.67 2.57 ± 2.94 0.43 Deceased at ICU 14 (9.7%) – – – Laboratory results Hemoglobin (g/dl) – admission 9.9 ± 1.3 9.9 ± 1.4 9.9 ± 1.3 0.94 Hemoglobin (g/dl) – last 8.7 ± 1.1 8.5 ± 1.0 8.8 ± 1.1 0.26 Leukocytes (Mrd/l)—admission 13.3 ± 5.9 12.8 ± 4.5 13.7 ± 6.8 0.35 Leukocytes (Mrd/l)—last 9.8 ± 4.5 9.0 ± 4.0 10.4 ± 4.9 0.08 Thrombocytes (Mrd/l)—admission 202.1 ± 100.9 199.2 ± 71.1 204.5 ± 120.3 0.75 Thrombocytes (Mrd/l)—last 174.9 ± 77.6 174.4 ± 63.4 175.4 ± 88.7 0.94 Creatinine (mg/dl)- admission 1.2 ± 0.6 1.1 ± 0.6 1.3 ± 0.7 Creatinine (mg/dl)—last 1.5 ± 1.4 1.3 ± 1.3 1.7 ± 1.5 0.09 CRP (mg/l) – admission 58.9 ± 38.6 57.8 ± 40.3 56.0 ± 37.2 0.78 CRP (mg/l) – last 107.2 ± 51.0 100.8 ± 48.7 112.7 ± 52.7 0.18 INR – admission 1.1 ± 0.2 1.1 ± 0.1 1.1 ± 0.2 0.72 INR – last 1.1 ± 0.2 1.1 ± 0.9 1.1 ± 0.2 0.12 Blood gas analysis paO2 (mmHg) – admission 101.6 ± 41.9 100.5 ± 41.8 102.5 ± 42.3 0.80 paO2 (mmHg) – last 87.0 ± 25.5 81.9 ± 21.9 91.6 ± 27.7 pvO2 (mmHg) – admission 37.8 ± 20.0 38.2 ± 19.4 37.5 ± 21.1 0.90 pvO2 (mmHg) – last 349.9 ± 202.6 377.6 ± 209.3 330.2 ± 203.1 0.58 pCO2 (mmHg) – admission 42.3 ± 42.3 40.8 ± 6.2 43.5 ± 8.9 pCO2 (mmHg) – last 39.5 ± 6.1 38.9 ± 4.9 39.9 ± 6.9 0.35 pH – admission 7.4 ± 0.1 7.4 ± 0.1 7.3 ± 0.1 0.08 pH – last 7,4 ± 0.1 7.4 ± 0.1 7.4 ± 0.1 Lactate (mmol/l) – admission 1.5 ± 1.2 1.2 ± 0.7 1.8 ± 1.5 Lactate (mmol/l) – last 1.2 ± 1.2 0.8 ± 0.4 1.5 ± 0.2
CRP C-reactive protein, INR international normalized ration, pa partial pressure in an arterial measurement, pv partial pressure in a venous measurement, O
Fourteen patients (10%) deceased whilst postoperatively treated at the ICU. Patients of the deceased cohort are grouped as 'mortality group' (MG), surviving patients as 'survival group' (SG) for further comparison. Whitin the ICU MG, need of ventilative support was significant higher (50%) as for the ICU SG (17%, p < 0.005). After ICU transfer to surgical wards, eighteen more patients (12%) deceased during hospitalization. Overall, 32 patients (22%) deceased during in-hospital care.
The in-hospital SG showed significant differences compared with the in-hospital MG regarding SAPS II (SG: 35 ± 10; MG: 47 ± 13; p < 0.001), SOFA (SG: 2.3 ± 2.5; MG: 5.1 ± 3.3; p < 0.001) as well as lactate at ICU admission (SG: 1.3 ± 0.8; MG: 2.3 ± 1.9; p < 0.005) and CCI (SG: 1.3 ± 1.4; MG: 2.1 ± 1.6; p < 0.005). Within the in-hospital MG 60% of patients (n = 19) relied on ventilative support at ICU admission whereas only 14% of the in-hospital SG (p < 0.005).
Overall, 78% of patients (n = 115) survived surgical treatment and in-hospital medical care. We transferred 55% of patients (n = 63) to receive specialized geriatric rehabilitation. 53% of patients (34 of 64) who were previously self-supporting at a private living space could return home within the postoperative year.
During the 28- and 90-day follow-up, 6 and 18 additional patients of those 115 patients who survived the in-hospital phase passed away. Both, the 28- and 90-day MGs showed prolonged ventilative support during their intensive care period (p < 0.05) compared to surviving patients. The 28-day SG showed significant differences compared to the 28-day MG in terms of SAPS II (SG: 35 ± 9.6; MG: 45 ± 13; p < 0.001) and SOFA score (SG: 2.2 ± 2.5; MG: 4.7 ± 3.5; p < 0.001) as well as lactate levels at ICU admission (SG: 1.2 ± 1; MG: 1.8 ± 2; p < 0.003) and CCI (SG: 1.3 ± 1.4; MG: 2 ± 1.5; p = 0.013). In a multiple binary logistic regression analysis, only SAPS II showed an independent positive predictive value for 28-day mortality (p = 0.002, R
One year survival of our cohort was 44% (n = 65) as seen in Fig. 2.
Graph: Figure 2Shown is the one-year survival analysis as Kaplan–Meier plot. Probability of Survival in %; time in days.
At ICU admission, patients belonging to the one-year SG showed a lower maximal heart frequency of 82 ± 19 beats per minute (bpm) than the MG with 91 ± 29 bpm (p < 0.05) as well as higher mean arterial pressure: SG 90 ± 19 mmHg and MG 83 ± 21 mmHg (p < 0.05) as seen in Table 2. The SG also showed differences compared with the MG regarding SAPS II (SG: 34.6 ± 9; MG: 40 SD ± 13; p < 0.003), SOFA score after 24 h (SG: 2.0 ± 2; MG: 3.5 ± 3; p < 0.002 but not respiratory support as seen in Table 3. Lactate level at ICU admission (SG: 1.1 ± 0.7; MG: 1.7 ± 1.5; p < 0.005) and CCI (SG: 0.9 ± 1; MG: 1.9 ± 2; p < 0.001) also showed significant differences. An advance directive was available in 17% of patients (n = 25) and had no influence on one-year survival rates.
Table 3 ICU Characteristics of patients with proximal femur fracture. Shown are values regarding disease severity and respiratory support.
Variables All 365d Survival Deceased before d 365 d Disease severity SAPS II – admission (pts.) 37.7 ± 11.6 34.6 ± 9.2 40.1 ± 12.6 SOFA – admission (pts.) 2.9 ± 3.0 2.1 ± 2.5 3.5 ± 3.2 SOFA – 24h (pts.) 2.9 ± 3.2 2.0 ± 2.2 3.6 ± 3.7 Respiratory support Invasive MV 24, 16.6% 8, 12.5% 16, 19,8% 0.269 Duration of MV (days) 0.71 ± 0.82 0.47 ± 0.60 0.83 ± 0.90 0.312
Significant are in value [bold]. SAPS II simplified acute physiology score, SOFA sequential organ failure assessment score, pts points, MV mechanical ventilation. Data are expressed as n (%).
Table 3 depicts further values regarding disease severity and respiratory support.
The binary logistic regression model to investigate predictors of 365-day mortality was statistically significant (χ
Table 4 Logistic regression for independent predictors of mortality in patients with PFF. Regression with "stepwise backwards" method.
Logistic regression Predictor β (SE) OR [95% CI] Final model SOFA 0.037 0.15 (0.072) 1.162 [1.009 – 1.338] Lactate 0.070 1.60 (0.883) 4.960 [0.878 – 28.014] Charlson comorbidity index 0.004 2.50 (0.880) 12.181 [2.172 – 68.324] Dementia 0.231 − 0.53 (0.439) 0.591 [0.25 – 1.398] Overall model evaluation Omnibus test < 0.001 Goodness-of-fit test Hosmer–Lemeshow test 0.480
OR odds ratio; CI confidence interval; SOFA Sepsis-related organ failure assessment score. *Charlson Comorbidity Index, Lactate at admission were transformed prior to logistic regression analysis (Log base 10—logarithm).
In this large study of 148 very elderly patients ≥ 90 years who underwent surgically treatment for PFFs or periprosthetic femur fractures and were consecutively admitted to the ICU, our main findings were: (A) 78% of patients survived surgical procedure and hospitalization, including ICU treatment; (B) 53% of patients who resided in private dwellings prior to admission were able to return home; and (C) Independent predictors of one-year all-cause mortality included higher CCI and SOFA scores at ICU admission (Fig. 3).
Graph: Figure 3Shown is a concise overview of important study characteristics and results supporting further interpretation.
To our knowledge this is the first comprehensive study focusing on a very vulnerable sub-cohort of very elderly patients with PFFs treated in the intensive care unit (ICU). We found an all-cause in-hospital mortality of 22% and one-year mortality of 56% in the present study. Of interest is, this is twice as high as reported in contemporary European studies of elderly patients with PFFs: In a prospective multicenter study in Spain (997 cases, 2014 – 2016, mean age 84 years) in-hospital and 60-day mortality was reported to be 2 and 11% respectively[
The initially higher mortality rate in our study can be attributed to the specific cohort and institutional factors. First, our study only included patients treated in the ICU, leading to a selection of predominantly critically ill nonagenarians with a higher susceptibility to rapid health deterioration[
As life expectancy increases worldwide, a growing number of individuals reaching the mile stone of 90 years, are expected to live for several more years. According to the German federal statistical office (DESTATIS) and the human mortality database report, in 2020, the average mortality rate for German individuals aged 94 (which aligns with the median age of the sample cohort) was reported to be 27%[
Several comorbidities and patient characteristics have previously been linked to increased mortality after PFFs, including male gender, dementia, cardiac disease, and renal dysfunction[
The increase in ICU admissions in recent time seems to be attributed to the heightened concern surrounding unplanned ICU admissions after transferring patients to the surgical ward, a circumstance associated with elevated postoperative morbidity and mortality rates[
While 78% of patients survive initial in-hospital treatment, nonagenarians face a higher mortality rate during follow-up with only 44% survival. An Italian cohort study of hospitalized nonagenarian patients (n = 124, median age of 93) showed a similar probability of being alive at one year (45%)[
Assessing patient risk after admission for PFFs is crucial in predicting adverse events and complications and guiding patients and their families. This is especially important in critical illness cases requiring ICU treatment. PFFs and their aftermath can cause significant psychological stress, particularly when family members are required to make decisions in cases of rapidly declining health or dementia. Identifying factors that support informed decision-making and accurate assessment of the situation is essential in guiding patients, families, and determining appropriate treatment and care. Of interest is, that patients had only a low number of advance directives which is in line with previous studies in the intensive care setting[
- The SOFA score demonstrated independent predictive value for one-year mortality (p 0.037) in our cohort, with a range of 0 – 12 and mean value of 3.5 ± 3.2 for the MG and 2.1 ± 2.5 for the SG. Although initially designed for patients with sepsis, recent data suggests similar accuracy in both surgical and non-surgical subjects[
16 ]. Clearly, ICU mortality is strongly associated with organ failure rate and severity which is tied to the SOFA score. A total SOFA score ≤ 4 indicates a high likelihood of discharge without adverse events, while a score ≥ 10 is associated with increased mortality. However, the range between these thresholds provides less precise discrimination, limiting the use of the SOFA score as a prognostic marker. A total SOFA score ≤ 4 indicates a high likelihood of discharge without adverse events, while a score ≥ 10 is associated with increased mortality. However, the range between these thresholds provides less precise discrimination, limiting the use of the SOFA score as a prognostic marker[44 ]. In a nutshell, it is clear that organ failure is associated with mortality, however, the lack of adequate discrimination at intermediate SOFA score values makes it an unreliable predictor of mortality[44 ]. The interpretation of the SOFA score should be based on its intended use, whether it is as a diagnostic tool, prognostic marker, or resource allocation aid. While it can provide valuable insight into patient severity and potential outcome, it should not be solely relied upon for prognostication. It can also aid in patient triaging and facilitate end-of-life discussions with families[44 ]. - CCI has been found to have a significant predictive value for mortality after one year (p 0.004). In the study cohort, the mean value was 1.9 ± 1.6 in the one-year MG and 0.09 ± 1 for the SG. With an aging population, hospitals are facing an increase of patients with PFFs and multiple comorbidities, but ICU treatment also allows more critically ill patients to receive surgical care. Recent studies have shown that CCI can accurately predict short-term and in-hospital mortality in PFF patients[
14 ],[45 ]. Comorbidities in patients with hip fracture have been shown to increased 30-day postoperative mortality[46 ]. Schrøder et. al highlighted a comorbidity-related disparity of quality of in-hospital care which unintendedly led to an impaired patient prognosis. Such patients were less likely to receive the totality of recommended care, and rehabilitation with early preoperative optimization and mobilization impacted most[47 ]. The level of care dependency was found to be a determinant of quality of life and also of survival rates among comorbid patients with COPD, chronic heart failure or chronic renal failure[48 ]. The impact of comorbidities on receiving total recommended care is not limited to patients with PFF, and should be used to highlight the need for tailored clinical initiatives to ensure optimal patient care and best chances of survival[47 ].
This study presents both strengths and limitations. Although the sample size is limited, it is the first extensive examination of the incidence of mortality among critically ill nonagenarians with PFFs. The single-center design restricts the generalizability to other healthcare settings, such as non-tertiary level hospitals. We acknowledge the disparities observed across nations which are shaped by diverse healthcare infrastructures, economic capacities, and cultural contexts and are often intertwined with the availability and allocation of resources. These differences underscore the importance of considering the resource landscape when interpreting and comparing outcomes on a global scale. To increase significance, it is recommended to validate the findings using a separate cohort in future studies. Additionally, the retrospective nature of the study and its focus on the ICU perspective restricts the available data on preoperative functional values, and there may be other influential factors impacting mortality that were not considered in this study. Also, only restricted demographic data regarding non-ICU admitted and conservatively treated patiens were available for analysis.
Our study demonstrates the utility of using CCI and SOFA scores at ICU admission as prognostic indicators in a cohort of 148 critically ill very elderly patients who underwent surgical treatment for PFFs. These scores can provide insight into the severity of illness and potential outcomes, which can inform resource allocation, prioritize endangered patients, and aid in end-of-life discussions and planning with patients and their families.
We acknowledge financial support from the Open Access Publication Fund of UKE—Universitätsklinikum Hamburg-Eppendorf and DFG—German Reserch Foundation. A.H. is funded by the Deutsche Forschungsgesellschaft (DFG, German Research Foundation)—project number 526240791.
A.H.: conception and design, analysis and interpretation of data, article drafting. J.M.: conception and design, critical revision, supervision. A.S.: analysis and interpretation of data, critical revision. F.F.: conception and design, critical revision. R.D.: acquisition of data, critical revision. P.T.: acquisition of data, critical revision, K.-H.F.: resources, conception and design, critical revision. S.K.: resources, conception and design, critical revision. J.H.: acquisition of data, critical revision. D.T.: conception and design, analysis and interpretation of data, critical revision, supervision. K.R.: conception and design, analysis and interpretation of data, critical revision, supervision.
Open Access funding enabled and organized by Projekt DEAL.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
The authors declare no competing interests.
• MG
- Mortality group
• SG
- Survival group
• CCI
- Charlson comorbidity index
• SOFA
- Sequential organ failure assessment
• SAPS II
- Simplified acute physiology score II
• ICU
- Intensive care unit
• PFF
- Proximal femur fractures
• SD
- Standard deviation
• OR
- Odds ratio
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
By Annika Heuer; Jakob Müller; André Strahl; Florian Fensky; Rikus Daniels; Pauline Theile; Karl-Heinz Frosch; Stefan Kluge; Jan Hubert; Darius Thiesen and Kevin Roedl
Reported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author