Abstract
This study aims to investigate the association between preoperative Prognostic Nutritional Index (PNI) and short-term clinical outcomes – including 30-day complications and readmission – after unicompartmental knee arthroplasty (UKA) in patients with medial compartment knee osteoarthritis (KOA). This was a retrospective single-center study, enrolling patients aged ≥60 years with medial KOA who underwent UKA between January 2022 and December 2024. The main outcomes were 30-day postoperative complications, length of hospital stay, and unplanned readmission within 30 days after surgery. Demographic, clinical, and laboratory data were collected. The optimal PNI cutoff was determined using a generalized additive model for the risk of overall complications, whereby patients were divided into low and high PNI groups. Multivariate analyses of the relationship between PNI and postoperative outcomes were adjusted for demographic and clinical covariates identified through univariate analysis (P <.10) and those deemed clinically relevant based on evidence from prior studies. Of the 507 patients (36.1% male, mean age 65.0 ± 3.5 years), 52 (10.3%) experienced complications within 30 days postoperatively, and 20 (3.9%) were readmitted. The most common complications were wound-healing problems (3.2%), deep venous thrombosis (2.4%), and surgical site infection (1.8%). The optimal PNI cutoff was 48.9. Patients with PNI <48.9 had significantly higher risks of overall complications (OR = 2.28, 95% CI: 1.11–4.67; P = .025) and readmission (OR = 4.84, 95% CI: 1.78–13.13; P = .002) compared to those with higher PNI. No significant difference in total or postoperative length of stay was observed between groups. Lower preoperative PNI is independently associated with increased risk of postoperative complications and readmission after UKA in patients with medial KOA. Routine preoperative assessment of PNI may aid in identifying high-risk patients and optimizing perioperative care to improve clinical outcomes.
Keywords: knee osteoarthritis, postoperative complications, prognostic nutritional index, readmission, risk stratification, unicompartmental knee arthroplasty
1. Introduction
Knee osteoarthritis (KOA) is a common degenerative joint disease that primarily affects the medial compartment of the knee, leading to pain, functional limitation, and diminished quality of life among middle-aged and elderly individuals.[1] For selected patients with isolated medial compartment involvement, unicompartmental knee arthroplasty (UKA) has become a widely accepted surgical option, demonstrating advantages such as faster recovery, lower perioperative morbidity, and improved patient satisfaction compared to total knee arthroplasty.[2–4] However, despite improvements in implant design and surgical techniques, perioperative complications and early readmissions remain relevant concerns,[5–7] particularly as the indications for UKA continue to expand to more diverse patient populations.
In recent years, preoperative nutritional status has been increasingly recognized as a critical determinant of postoperative outcomes in orthopedic surgery. According to literature, prevalence of malnutrition among patients with KOA ranges from approximately 10% to 28%, depending on the assessment tool and population studied.[8,9] Malnutrition, even in the absence of overt clinical signs, has been linked to higher rates of wound-healing problems, perioperative joint infection, longer hospital stays, and increased risk of short-term complications.[9,10] The Prognostic Nutritional Index (PNI), which combines serum albumin concentration and total lymphocyte count, serves as a simple and objective biomarker to assess a patient’s immunonutritional status. Numerous studies in various surgical specialties have demonstrated that a low PNI is associated with increased morbidity and mortality, and recent data suggest it may be an independent predictor of adverse outcomes following joint arthroplasty as well.[11,12] However, its prognostic value in the setting of UKA, especially for medial KOA, remains largely unexplored. In addition, while previous research has investigated the impact of demographic and clinical risk factors – including body mass index (BMI), comorbidity burden, and perioperative management – on short-term outcomes after UKA.[13–15] However, few studies have specifically addressed the role of preoperative nutritional status as assessed by the PNI. Moreover, most available evidence pertains to total knee or hip arthroplasty, leaving a gap in the literature regarding the utility of PNI for risk stratification in patients undergoing UKA for medial KOA.
This study aimed to examine whether preoperative PNI is associated with perioperative outcomes – including 30-day complications, hospital stay, and unplanned readmissions – in patients undergoing UKA for medial compartment osteoarthritis. We hypothesized that a lower PNI would predict a higher risk of adverse postoperative events.
2. Methods
This retrospective single-center study was conducted at Jizhong Energy Xingtai Mining Group General Hospital and was approved by the institutional ethics committee. Owing to the retrospective nature of the study and anonymization of patient data, the requirement for informed consent was waived. All procedures were performed in accordance with the Declaration of Helsinki.
Patients aged 60 years or older, diagnosed with medial KOA who underwent UKA between January 2022 and December 2024 at our institution were screened. Inclusion criteria were: a confirmed diagnosis of medial KOA, undergoing primary UKA; availability of preoperative laboratory data, including serum albumin and lymphocyte counts within 2 days before surgery; complete demographic, clinical, and follow-up data (at least 30 days postoperatively). Exclusion criteria were: prior surgery or revision in the affected knee; active or recent local or systemic infection; use of immunosuppressive agents or chronic glucocorticoids; receipt of blood transfusion or albumin supplementation prior to surgery; missing key laboratory or outcome data.
3. Data collection
All data were retrospectively extracted from electronic medical records and follow-up databases by 2 investigators (C.Z. and S.L.). These data included Demographic and socioeconomic characteristics (Age, sex, BMI, and place of residence), functional status (independent or dependent), medical history and comorbidities (history of hypertension, diabetes mellitus, chronic obstructive pulmonary disease, cardiovascular disease, liver disease, renal insufficiency, hyperlipidemia, hypercholesterolemia, previous surgical procedures, alcohol consumption, and smoking status, surgery-related variables (American Society of Anesthesiologists classification, laterality (side of operation), time from admission to surgery, anesthesia technique, total surgical duration (in minutes), and estimated intraoperative blood loss and laboratory results at admission (serum albumin, lymphocyte count, fasting blood glucose, red blood cell count, white blood cell count, neutrophil count, platelet count, hemoglobin concentration, hematocrit, creatinine, and serum sodium concentration.
Functional status was defined as “independent” if the patient could perform daily activities without assistance, and “dependent” if any help or assistive devices were needed.
4. Exposure and outcome
Laboratory blood sampling and testing were performed according to the manufacturer’s instructions, typically on the first morning after admission. The PNI was calculated using the following formula: PNI = 10 × serum albumin (g/dL) + 5 × total lymphocyte count (10⁹/L).[16]
A typical reference range for PNI is 50 to 65 in adults, with values below 40 generally regarded as indicative of malnutrition.[17] For this study, PNI was used both as a continuous variable and for dichotomization based on an optimal cutoff value determined from the study cohort.
The primary outcomes of interest in this study included the occurrence of any complication within 30 days postoperatively; total hospital length of stay (LOS), calculated as the number of days from admission to discharge; postoperative LOS, calculated as the number of days from the date of surgery to discharge; and unplanned readmission within 30 days following surgery for any reason.
A “30-day complication” was defined as the occurrence of any of the following events within 30 days after the index surgical procedure: surgical site infection (SSI) (either superficial, deep incisional, or organ/space), wound dehiscence, respiratory complications (including pneumonia, unexpected need for intubation, or prolonged mechanical ventilation exceeding 48 hours), venous thromboembolism (pulmonary embolism or deep vein thrombosis requiring intervention), progressive renal impairment, urinary tract infection, acute neurological events (stroke or cerebrovascular accident), cardiac events (such as myocardial infarction), transfusion of blood products within 72 hours after surgery, or infectious complications including sepsis, septic shock, or Clostridium difficile infection. Complications were identified based on clinical, laboratory, and radiological criteria, and verified by review of medical records and follow-up notes.
5. Statistical analysis
Continuous variables were expressed as mean ± standard deviation or median with interquartile range (IQR) as appropriate; categorical variables as counts and percentages. Normality was assessed by the Kolmogorov–Smirnov test. Between-group comparisons were performed using Student t test or Mann–Whitney U test for continuous variables, and Chi-square or Fisher exact test for categorical variables.
To explore the nonlinear association between preoperative PNI and the incidence of overall complications, a generalized additive model (GAM) with smoothing splines was fitted, adjusting for relevant confounders. The fitted curve was visually inspected to assess the dose-response relationship. The cutoff value for PNI was determined as the point on the GAM-fitted risk curve where the log odds ratio (log [OR]) equals zero (i.e., where OR = 1), whereby patients were stratified into “low PNI” and “high PNI” groups.
Comparisons of demographic, clinical, and laboratory characteristics between the low and high PNI groups were conducted using appropriate statistical tests. To minimize confounding, variables with P <.10 in the univariate analysis and clinically important factors established in prior studies were entered into multivariate models to examine the independent association between PNI and categorical outcomes. Adjusted ORs with corresponding 95% confidence intervals were calculated. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test. For continuous outcome variables, such as total LOS and postoperative LOS, multivariate linear regression models were utilized. Statistical analyses were performed with R software (version 4.2.0, R Foundation for Statistical Computing, Vienna, Austria) and SPSS (version 27.0, IBM Corp., Armonk), and a 2-sided P value <.05 was considered statistically significant.
Cases with missing values for key variables (exposure or outcome) were excluded from all analyses. No imputation methods were applied, as the proportion of missing data was low and unlikely to introduce significant bias.
6. Results
Among 697 screened patients, 507 fulfilled the inclusion criteria and were included in the final analysis (Fig. 1); 190 were excluded according to predefined criteria. Of those 507 eligibly included, 183 were males (36.1%) and 324 were females (63.9%), with a mean age of 65.0 ± 3.5 years (range, 60 to 79 years). Within 30 days postoperatively, 52 patients experienced complications, resulting in an overall complication rate of 10.3% (95% CI: 7.6%–12.9%). The most frequent complication was wound-healing problems (n = 16, 3.2%), followed by deep venous thrombosis of the lower extremity (n = 12, 2.4%), SSI (n = 9, 1.8%), and urinary tract infection (n = 6, 1.2%). A total of 20 patients (3.9%, 95% CI: 2.3%–5.6%) were readmitted for various reasons, including wound infection, wound complications, symptomatic deep venous thrombosis (DVT), persistent pain, joint stiffness, or pneumonia.
Figure 1.
Generalized additive model illustrates the nonlinear relationship between the prognostic nutritional index and the risk of the composite of complications, as expressed by the log odds ratio. As PNI increases from lower values (around 35) to approximately 52.5, the log(OR) for the outcome decreases steeply. The intersection of the fitted curve with log(OR) = 0 (i.e., OR = 1) occurs at PNI = 48.9, as indicated by the vertical dashed line, which was identified as the optimal cutoff for dichotomizing PNI into high and low groups. PNI = prognostic nutritional index.
The mean serum albumin level was 42.1 ± 3.7 g/L, mean lymphocyte count was 1.8 ± 0.6 × 10⁹/L, and mean PNI was 50.5 ± 5.6. As shown in Figure 2, the multivariate GAM analysis revealed a significant nonlinear inverse association between PNI and the risk of overall complications (P for nonlinearity = 0.002). As PNI increased from approximately 35 to 52.5, the log(OR) for complications declined sharply. The fitted curve crossed log(OR) = 0 (i.e., OR = 1) at a PNI of 48.9, which was thus selected as the optimal cutoff to dichotomize patients into low and high PNI groups. Due to the relatively small number of readmission events (n = 20) and potential risk of overfitting, we did not model a nonlinear relationship between PNI and readmission; instead, the same PNI cutoff of 48.9 was used in all subsequent analyses for both categorical and continuous outcomes.
Figure 2.
Flowchart of patient selection for the study. A total of 697 patients who underwent UKA for medial compartment osteoarthritis were initially screened for eligibility. After excluding patients with prior knee surgery or revision (n = 37), use of immunosuppressive agents or chronic glucocorticoids (n = 63), albumin supplementation before surgery (n = 7), active or recent infection (n = 11), receipt of blood transfusion prior to surgery (n = 6), and incomplete key clinical or laboratory data (n = 66), 507 patients were included in the final analysis. According to the optimal cutoff value of preoperative PNI (PNI = 48.9), patients were divided into 2 groups: high-PNI group (PNI ≥48.9, n = 324) and low-PNI group (PNI <48.9, n = 183). PNI = prognostic nutritional index, UKA = unicompartmental knee arthroplasty.
Compared with patients with PNI ≥48.9, those with PNI <48.9 showed a trend toward lower BMI (26.3 ± 3.2 vs 26.9 ± 3.5, P = .064), higher prevalence of chronic obstructive pulmonary disease (8.7% vs 4.9%, P = .091), a greater proportion of higher American Society of Anesthesiologists grade (III or above: 15.8% vs 9.9%), less intraoperative blood loss (112.5 ± 35.4 vs 126.5 ± 63.7 ml), and significantly lower platelet and red blood cell counts, hematocrit, hemoglobin (P, total cholesterol, triglycerides, and creatinine, as well as higher hs-CRP concentrations. (Table 1)
Table 1.
Comparisons of demographic, clinical characteristics and laboratory testing results between high- and low-PNI groups.
| Variables | PNI ≥48.9 (n = 324) | PNI <48.9 (n = 183) | P |
|---|---|---|---|
| Age (yr) | 65.0 ± 3.4 | 66.2 ± 3.8 | .413 |
| Sex (male) | 120 (37.0) | 63 (34.4) | .557 |
| Residence (rural) | 173 (53.4) | 97 (53.0) | .699 |
| BMI (kg/m2) | 26.9 ± 3.5 | 26.3 ± 3.2 | .064 |
| <24.0 | 66 (20.4) | 44 (24.0) | .281 |
| 24.0–27.9 | 152 (46.9) | 91 (49.7) | |
| 28.0–31.9 | 106 (32.7) | 48 (26.2) | |
| Functional status | |||
| Independent | 316 (97.5) | 180 (98.4) | .538 |
| Dependent | 8 (2.5) | 3 (1.6) | |
| Hypertension | 122 (35.9) | 70 (36.5) | .894 |
| Diabetes mellitus | 32 (9.9) | 16 (8.7) | .675 |
| Cardiovascular disease | 43 (13.3) | 16 (8.7) | .127 |
| COPD | 16 (4.9) | 16 (8.7) | .091 |
| Liver disease | 27 (8.3) | 14 (7.7) | .786 |
| Smoking | |||
| Current | 35 (10.8) | 14 (7.7) | .249 |
| Former or never | 289 (89.2) | 169 (92.3) | |
| Alcohol consumption | |||
| Current | 115 (35.5) | 55 (32.4) | .213 |
| Former or never | 209 (64.5) | 115 (67.6) | |
| Renal insufficiency | 13 (4.0) | 12 (6.6) | .204 |
| History of any operation in lower extremity | 46 (14.2) | 18 (9.8) | .156 |
| ASA classification | |||
| Ⅰ or II | 292 (90.1) | 154 (84.2) | .047 |
| III or more | 32 (9.9) | 29 (15.8) | |
| Sidedness | |||
| Left | 167 (51.5) | 93 (50.8) | .876 |
| Right | 157 (48.5) | 90 (49.2) | |
| Time from admission to operation (d) | 2.5 ± 1.6 | 2.8 ± 1.6 | .116 |
| Anesthesia technique | |||
| General | 188 (63.9) | 106 (57.9) | .982 |
| Regional | 136 (36.1) | 77 (42.1) | |
| Surgical duration (min) | |||
| <120 | 219 (63.1) | 128 (69.9) | .584 |
| ≥120 | 105 (36.9) | 55 (30.1) | |
| Intraoperative bleeding (ml) | 126.5 ± 63.7 | 112.5 ± 35.4 | .006 |
| FBG (mmol/L) | 5.6 ± 1.3 | 5.6 ± 1.5 | .974 |
| Platelet count (*1012/L) | 234.6 ± 52.1 | 211.5 ± 67.1 | <.001 |
| RBC count (*1012/L) | 4.5 ± 0.4 | 4.0 ± 0.5 | <.001 |
| Hemoglobin (g/L) | 134.8 ± 11.8 | 118.8 ± 15.1 | <.001 |
| Sodium concentration (mmol/L) | 140.5 ± 2.0 | 140.6 ± 2.2 | .316 |
| WBC count (*109/L) | 6.1 ± 1.4 | 6.2 ± 1.6 | .989 |
| HCRP | 5.3 ± 12.2 | 29.9 ± 35.1 | <.001 |
| Hematocrit (%) | 40.7 ± 3.5 | 35.6 ± 4.5 | <.001 |
| Creatine (mmol/L) | 62.9 ± 12.6 | 59.8 ± 11.1 | .006 |
| TC | 5.0 ± 1.0 | 4.4 ± 0.7 | <.001 |
| TG | 1.8 ± 1.1 | 1.3 ± 0.8 | <.001 |
| Albumin (g/L) | 43.9 ± 2.7 | 37.9 ± 2.5 | <.001 |
| Lymphocyte count (*109/L) | 2.0 ± 0.5 | 1.4 ± 0.4 | <.001 |
| Overall length of hospital stay (d) | 7.6 ± 2.9 | 7.5 ± 2.6 | .924 |
| Postoperative length of hospital stay (d) | 5.0 ± 2.0 | 4.7 ± 1.7 | .147 |
ASA = American Society of Anesthesiologists, BMI = body mass index, COPD = chronic obstructive pulmonary disease, FBG = fasting blood glucose, HCRP = high-sensitive C-reaction protein, PNI = prognostic nutritional index, RBC = red blood cell, TC = cholesterol, TG = total triglyceride, WBC = white blood cell.
Stepwise multivariate logistic regression demonstrated that PNI <48.9 was independently associated with higher overall complication risk (OR = 2.28; 95% CI: 1.11–4.67; P = .025, Table 2) and an increased risk of readmission (OR = 4.84, 95% CI: 1.78–13.13; P = .002, Table 3). Both models showed acceptable goodness-of-fit (Hosmer–Lemeshow P >.05).
Table 2.
Multivariate analysis of PNI in association with postoperative overall complications after unicompartmental arthroplasty for KOA.*
| Variables | β | OR and 95% CI | * P |
|---|---|---|---|
| PNI (<48.9.0 vs ≥48.9) | 0.832 | 2.28 (1.11–4.67) | .025 |
| TC (each mmol/L increment) | 0.387 | 1.47 (1.07–2.02) | .017 |
| HCRP (each mmol/L increment) | 0.015 | 1.02 (1.01–1.03) | .002 |
| Surgical duration (≥120 vs <120 min) | 1.316 | 3.73 (1.60–8.70) | .002 |
β, regression coefficient.
ASA = American Society of Anesthesiologists (classification/score), BMI = body mass index, CI = confidence interval, COPD = chronic obstructive pulmonary disease, HCRP = high-sensitive C-reaction protein, KOA = knee osteoarthritis, OR = odds ratio, PNI = prognostic nutritional index, RBC = red blood cell (count), TG = total triglyceride.
The multivariate analysis was performed, adjusted for age, sex, BMI, functional status, smoking, diabetes, COPD, ASA classification, estimated blood loss, TG, RBC count, hemoglobin, platelet count, creatine, hematocrit.
Table 3.
Multivariate analysis of PNI in association with unplanned readmission after unicompartmental arthroplasty for KOA.*
| Variables | β | OR and 95% CI | * P |
|---|---|---|---|
| Age (each year increment) | 0.064 | 1.07 (0.99–1.14) | .073 |
| PNI (<48.9.0 vs ≥48.9) | 1.576 | 4.84 (1.78–13.13) | .002 |
| Diabetes | 1.238 | 3.45 (1.03–11.50) | .044 |
| Surgical duration (≥120 vs <120 min) | 1.015 | 2.74 (1.23–6.47) | .012 |
β, regression coefficient.
ASA = American Society of Anesthesiologists (classification/score), BMI = body mass index, CI = confidence interval, COPD = chronic obstructive pulmonary disease, KOA = knee osteoarthritis, OR = odds ratio, PNI = prognostic nutritional index, RBC = red blood cell (count).
The multivariate analysis was performed, adjusted for age, sex, BMI, functional status, smoking, diabetes, COPD, ASA classification, estimated blood loss, TG, RBC count, hemoglobin, platelet count, creatine, hematocrit.
No significant differences were observed between the high- and low-PNI groups in either total or postoperative length of hospital stay. This was consistent across both univariate analyses (total LOS: 7.5 ± 2.6 vs 7.6 ± 2.9 days, P = .924; postoperative LOS: 4.7 ± 1.7 vs 5.0 ± 2.0 days, P = .147) and multivariate linear regression (total LOS: β = 0.043, 95% CI:–0.370 to 0.873, P = .427; postoperative LOS: β = –0.012, 95% CI:–0.487 to 0.389, P = .827).
7. Discussion
In this retrospective study of 507 patients undergoing UKA for medial compartment osteoarthritis, we observed that lower preoperative PNI (<48.9) was significantly associated with an increased risk of postoperative complications and hospital readmission within 30 days. There were no significant differences between groups regarding total or postoperative length of hospital stay.
The underlying mechanisms by which a lower PNI predisposes patients to complications and readmission are likely multifactorial. PNI integrates serum albumin and peripheral lymphocyte count, serving as a composite marker of both nutritional and immune status.[11] Malnutrition impairs collagen synthesis, tissue regeneration, and angiogenesis, all of which are essential for wound healing.[18] Hypoalbuminemia may also indicate chronic inflammation and a catabolic state, further compromising the patient’s ability to recover from surgical trauma.[19] In addition, lymphopenia reflects impaired cellular immunity, making patients more vulnerable to infections and delaying recover.[20] Such a pro-inflammatory and immunosuppressed milieu may increase the risk not only of wound complications and SSIs but also of thromboembolic events and delayed rehabilitation, contributing to early hospital readmission. Recent meta-analyses have demonstrated that malnutrition and hypoalbuminemia are independently associated with both medical and surgical complications following joint arthroplasty, supporting our findings.[21–23]
Our results are consistent with, and expand upon, recent studies that have explored the impact of nutritional status on arthroplasty outcomes. Several large database and cohort studies in the past several years have highlighted the prognostic utility of preoperative nutritional indices such as PNI, the geriatric nutritional risk index, the Controlling Nutritional Status (CONUT) score and the protein energy malnutrition (PEM) assessed using serum albumin and total lymphocyte count.[24–26] For example, Eminovic et al reported that patients with PEM experienced a markedly higher incidence of postoperative complications – 44% within 6 months – compared to only 7.8% in non-PEM patients. Furthermore, Li et al.[25] demonstrated that moderate-to-severe malnutrition at first or second stage indicated by the CONUT score is associated with 5.86-fold or 12.15-fold increased risk treatment failure caused by periprosthetic joint infection. A recent meta-analysis by Chen et al corroborated the role of malnutrition, as assessed by various nutritional indices, as a key predictor of SSI in both total knee arthroplasty and THA.[21] However, few studies have specifically addressed the UKA population; our study adds to the literature by showing that the predictive value of PNI is robust even in patients undergoing less invasive unicompartmental procedures. In contrast to some earlier reports, we did not find a significant relationship between PNI and LOS, possibly due to standardized postoperative pathways and early mobilization protocols in our institution. Overall, our findings are in line with a growing body of evidence that emphasizes the role of nutritional assessment in risk stratification for arthroplasty patients.
Our findings may have important implications for the perioperative care of patients undergoing UKA for KOA. Routine preoperative assessment of PNI is straightforward, cost-effective, and may help identify high-risk patients who would benefit from targeted nutritional interventions prior to UKA. By stratifying patients according to nutritional risk, clinicians can optimize perioperative care, including individualized nutrition supplementation, closer monitoring for complications, and tailored rehabilitation plans. Integration of PNI into preoperative screening may promote shared decision-making and timely intervention, ultimately reducing the incidence of postoperative complications and hospital readmission. Clinical practice guidelines for joint arthroplasty should therefore consider routinely including nutritional status – especially PNI – in perioperative assessment. In addition, as malnutrition remains a modifiable risk factor, multidisciplinary management involving nutritionists, orthopedic surgeons, and rehabilitation specialists is warranted to improve patient outcomes.[27]
This study has several strengths, including a relatively large sample size, comprehensive assessment of both clinical and laboratory variables, and the application of advanced statistical modeling (GAM) to model the relationship and determine the optimal PNI cutoff. However, several limitations must be acknowledged. First, the retrospective, single-center design may limit generalizability and introduce selection bias. Second, while PNI is a validated and practical index, it may not capture the full spectrum of nutritional or inflammatory status, and other unmeasured confounders such as vitamin deficiencies or frailty may influence outcomes. Third, the relatively small number of readmission events limited our ability to explore more granular associations and prevented further stratified analyses. Finally, causality cannot be inferred from observational data, and prospective studies are needed to validate our findings.
In summary, our study demonstrates that lower preoperative PNI is independently associated with increased risks of short-term complications and readmission following UKA. The results underscore the value of preoperative nutritional assessment in optimizing patient selection and perioperative management. Future multicenter prospective studies and interventional trials are needed to confirm the benefits of nutritional optimization and to establish evidence-based guidelines for perioperative nutrition in joint arthroplasty.
Acknowledgments
We are grateful to T.M. of the Department of Hand Surgery for his kind assistance.
Author contributions
Conceptualization: Chaozheng Li.
Data curation: Shixian Li.
Formal analysis: Chenyang Zhang.
Investigation: Chenyang Zhang, Shixian Li.
Methodology: Chenyang Zhang, Shixian Li, Hexuan Xin.
Resources: Shuangtao Chen.
Software: Shixian Li, Hexuan Xin, Shuangtao Chen.
Validation: Hexuan Xin, Shuangtao Chen.
Writing – original draft: Chenyang Zhang.
Writing – review & editing: Chaozheng Li.
Abbreviations:
- ASA
- American Society of Anesthesiologists (classification/score)
- BMI
- body mass index
- CI
- confidence interval
- CONUT
- controlling nutritional status (score)
- COPD
- chronic obstructive pulmonary disease
- DVT
- deep vein thrombosis
- FBG
- fasting blood glucose
- GAM
- generalized additive model
- GNRI
- geriatric nutritional risk index
- Hs-CRP
- hypersensitive C-reactive protein
- IQR
- interquartile range
- KOA
- knee osteoarthritis
- LOS
- length of stay
- OR
- odds ratio
- PEM
- protein energy malnutrition
- PNI
- prognostic nutritional index
- RBC
- red blood cell (count)
- SD
- standard deviation
- SSI
- surgical site infection
- THA
- total hip arthroplasty
- TKA
- total knee arthroplasty
- UKA
- unicompartmental knee arthroplasty
- WBC
- white blood cell (count)
This study was supported by the Xingtai Key Research and Development Program (No. 2024ZC154).
The study protocol was approved by the Ethics Committee of Jizhong Energy Xingtai Mining Group General Hospital, which waived the requirement for informed consent.
The authors have no conflicts of interests to disclose.
The data and materials used or analyzed during the study are not publicly available in accordance with our institutional policy, but are available from the corresponding author upon request.
How to cite this article: Zhang C, Li S, Xin H, Chen S, Li C. Association between preoperative prognostic nutritional index and perioperative outcomes after unicompartmental knee arthroplasty for medial knee osteoarthritis: A retrospective single-center study. Medicine 2026;105:5(e47324).
Contributor Information
Chenyang Zhang, Email: 823166882@qq.com.
Shixian Li, Email: lichaozheng2006@163.com.
Hexuan Xin, Email: xin.he.xuan@163.com.
Shuangtao Chen, Email: chenshuangtao@126.com.
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