Translate this page into:
Risk factors and predictive models for postoperative surgical site infection in patients with massive hemorrhage
⁎Corresponding author: Li-Min Zhang. azai2010@126.com
-
Received: ,
Accepted: ,
This article was originally published by Reed Elsevier India Pvt. Ltd. and was migrated to Scientific Scholar after the change of Publisher.
Abstract
Abstract
This study aimed to identify risk factors associated with postoperative surgical site infection (SSI) in patients experiencing massive hemorrhage and develop a predictive model.
A retrospective analysis of 121 orthopedic surgery patients and experienced massive hemorrhage was conducted. According to postoperative SSI occurrence, the patients were divided into two groups: the infection group (n = 12) and the non-infection group (n = 109). Clinical data were collected, and a predictive model was developed using logistic regression analysis in patients with massive hemorrhage.
Independent risk factors for postoperative SSI included ASA grade, urine volume, and type 2 diabetes. An area under the curve for the prediction of postoperative SSI based on the Receiver Operating Characteristic (ROC) curve for the risk score was 0.916.
Patients with a urine volume of ≥3.49 ml/kg/h, higher ASA grade, and type 2 diabetes are at an increased risk of developing postoperative SSI after experiencing massive hemorrhage.
Level III.
Keywords
Orthopedic surgery
Massive hemorrhage
Surgical site infections
Logistic regression
Predictive model
1 Introduction
Surgical site infection (SSI) poses a significant threat to patients with massive hemorrhage, often resulting in adverse outcomes and elevated mortality rates.1,2 SSI not only extends hospital stays and escalates healthcare expenses but also worsens the overall prognosis of affected patients.3–5 In general surgery patients, advanced age, obesity, diabetes, and prolonged surgical procedures have all been identified as risk factors for SSI.6–8 Consequently, a comprehensive understanding of the risk factors connected to SSI in patients with massive hemorrhage is paramount for the development of effective preventive strategies.
Massive hemorrhage is characterized by severe blood loss and tissue hypoperfusion, representing a major concern in surgical patient care.9 Swift fluid resuscitation and the restoration of circulating blood volume play vital roles in managing massive hemorrhage patients.10,11 Despite advancements in resuscitation methods and perioperative care, individuals with massive hemorrhage remain susceptible to a range of complications, including ischemia-reperfusion injuries to tissues and organs, as well as infections at the surgical site. The compromised integrity of tissues and impaired immune responses in these patients create an environment conducive to SSI development.12 There is, however, limited research addressing risks for SSI in patients with massive hemorrhage. Thus, the exploration of factors contributing to SSI in this specific population holds significant importance for the formulation of targeted interventions and the enhancement of patient outcomes.
We aim to understand the risk factors of SSI in patients who have undergone massive hemorrhage. By identifying precise factors that lead to SSI in this patient cohort, evidence-based strategies can be devised to reduce infection rates and enhance prognosis. The outcomes of this retrospective study are expected to offer valuable insights into the prevention and management of SSI in patients who experienced massive hemorrhage.
2 Objects and methods
2.1 Data sources and study subjects
A retrospective analysis was conducted on data from 121 patients who underwent cervical, thoracic, and lumbar vertebrae and joint revision surgery and experienced blood loss exceeding 800 ml. By analyzing institutional records that included blood loss exceeding 800 ml, in dividuals identified had undergone the orthopedic surgery between January 1 and December 31, 2022. Review of patient medical records involved collecting demographic details, comorbidities, preoperative hemoglobin, albumin, prealbumin, coagulation function, surgical details, and postoperative outcomes. The data was recorded and managed using Excel software. This study received approval from the Ethics Committee of Hebei Province Cangzhou Hospital of Integrated Traditional and Western Medicine.
2.2 Inclusion and exclusion criteria
The inclusion criteria comprised: (1) Patients undergoing surgery of the spine (cervical, thoracic, and lumbar vertebrae) and lower limbs (joint revision); (2) Patients with estimated intraoperative blood loss exceeding 800 ml; (3) Patients with complete clinical data; (4) Patients without absolute surgical contraindications after undergoing preoperative examination.
The exclusion criteria included: (1) Patients with preoperative infection; (2) Patients with severe trauma that required multiple surgical interventions (three or more times); (3) Patients with a history of prolonged use of immunosuppressive medications; (4) Those with malignant tumors, severe endocrine or immune system related diseases, or organ failure; (5) Patients with open fractures.
The diagnosis of surgical site infection adhered to the criteria defined for healthcare-associated infections based on the guidelines of the Disease Control and Prevention Center/National Healthcare Safety Network Surveillance Definitions and Reporting.13
Superficial incisional SSI criteria:
The term superficial incisional SSI refers to an infection that is restricted to the incision site and its subcutaneous tissues. To meet this definition, the patient had to exhibit at least one of the following criteria: (a) The superficial wound with purulent drainage; (b) Positive findings from microbial monitoring; (c) The existence of physical indicators or symptoms, such as local pain, tenderness, swelling, erythema, or fever, in the absence of culture-based or non-culture-based testing; (d) Diagnosis of superficial incisional SSI by a physician.
2.3 Deep incisional SSI criteria
This type of infection that penetrates into soft tissues including fascia and muscles is called deep incision SSI. At least one of the following criteria had to be met by the patient: (a) The deep wound with purulent drainage; (b) In the absence of a positive culture or non-culture-based test, fever (>38 °C) or localized pain/tenderness may be present; or (c) The presence of an abscess or other infection in the deep incision may be detected through gross anatomy, histopathology, or imaging.
2.4 Organ/space SSI criteria
An organ/space SSI is the result of the surgical procedure opening or manipulating points deeper in the body than the fascia and muscles. Among the requirements for this procedure, a patient had to meet one or more of the following requirements: (a) Discharge pus from drainage pipes placed in the organ or space (e.g., closed suction drainage system, open drain, T-tube drain, CT-guided drainage); (b) Positive results from microbial monitoring; or (c) The determination of equivocal or infection-definitive imaging evidence of an abscess or infection in the organ/space is done through gross anatomical examination, histopathologic examination, or gross anatomical examination with histology.
2.5 Data screening
Based on the established inclusion and exclusion criteria, we identified 121 patients with massive hemorrhage for this study. These patients were subsequently categorized into the infection group (n = 12) and the non-infection group (n = 109). A flowchart depicting the study's progression is provided in Fig. 1.
2.6 Statistical methods
Data were analyzed using the statistical software SPSS 22.0. In order to analyze categorical data, frequencies (percentages) were used, and chi-square or Fisher's exact tests were performed. Data with normally distributed continuous values (mean ± standard deviation) were analyzed with independent sample t-tests, whereas non-normally distributed values (median (interquartile range)) were analyzed with non-parametric tests. We developed a multivariate model based on binary logistic regression to identify independent risk factors associated with SSI, and we assessed model fitness using Hosmer-Lemeshow statistics. Based on the receiver operating characteristic (ROC) curve, sensitivity was plotted along the y-axis, and 1-specificity along the x-axis. A P-value <0.05 was statistically significant.
3 Results
3.1 Baseline demography
We examined the incidence of SSI in patients recovering from massive hemorrhage. There were 12 cases of SSI among the 121 patients studied, representing a 9.92 % infection rate. Based on the presence or absence of SSI after recovering from a massive hemorrhage, the patients were categorized into two groups: an infection group (n = 12) and a non-infection group (n = 109). Based on the univariate analysis, type 2 diabetes and American Society of Anesthesiologists (ASA) grade were significantly different between infection and non-infection groups (P < 0.05). Conversely, no significant differences were found in gender, age, height, weight, Body Mass Index (BMI), admission heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), chronic obstructive pulmonary disease (COPD), hypertension, coronary heart disease, old cerebral infarction and surgical site between the two groups (P > 0.05) (Table 1).
| Characteristics and Variables | All Patients (n = 121) | Infection Group (n = 12) | Non-Infection Group (n = 109) | P-Value |
| Sex | 0.185 | |||
| female, number (%) | 53 (43.8) | 3 (25.0) | 49 (45.0) | |
| male, number (%) | 68 (56.2) | 9 (75.0) | 60 (55.0) | |
| Age, years, median (IQR) | 59 (16) | 56 (21) | 60 (17) | 0.173 |
| Weight, kg, median (IQR) | 70 (15.5) | 70 (18.0) | 70 (15.0) | 0.737 |
| Height, cm, median (IQR) | 168 (13) | 170 (11) | 168 (12) | 0.389 |
| BMI, kg/m2, mean (SD) | 25.22 (3.63) | 24.47 (3.30) | 25.30 (3.67) | 0.457 |
| ASA, grade | 0.000 | |||
| II (%) | 94 (77.7) | 3 (25.0) | 91 (83.5) | |
| III (%) | 27 (22.3) | 9 (75.0) | 18 (16.5) | |
| SBP, mm Hg, median (IQR) | 138 (26.5) | 128 (54.0) | 138 (25.0) | 0.351 |
| DBP, mm Hg, median (IQR) | 80 (15.5) | 69 (31.0) | 80 (14.0) | 0.299 |
| Heart rate, beats/minute, median (IQR) | 75.0 (18.5) | 79.5 (27.0) | 75.0 (17.0) | 0.221 |
| Operative type | 0.976 | |||
| Lower limb operation, number (%) | 61 (50.4) | 6 (50.0) | 55 (50.5) | |
| Spinal surgery (%) | 60 (49.6) | 6 (50.0) | 54 (49.5) | |
| Hypertension, number (%) | 62 (51.2) | 4 (33.3) | 58 (53.2) | 0.191 |
| Coronary heart disease, number (%) | 26 (21.5) | 2 (16.7) | 24 (22.0) | 1.000 |
| Type 2 diabetes, number (%) | 13 (10.7) | 4 (33.3) | 9 (8.3) | 0.025 |
| Old cerebral infarction, number (%) | 10 (8.3) | 1 (8.3) | 9 (8.3) | 1.000 |
| COPD, number (%) | 5 (4.1) | 1 (8.3) | 4 (3.7) | 0.412 |
3.2 Characteristics of perioperative indicators
There were no significant differences in hemoglobin, albumin, prealbumin, and coagulation function between the two groups prior to surgery (P > 0.05). Furthermore, no significant differences were found in regards to estimated intraoperative blood loss, urine volume, crystal infusion volume, red blood cell infusion volume, Hydroxyethyl starch 130/0.4 infusion volume, surgical duration, anesthesia time, as well as postoperative serum creatinine and urea nitrogen between the two groups (P > 0.05) (Table 2).
| Characteristics and Variables | All Patients (n = 121) | Infection Group (n = 12) | Non-Infection Group (n = 109) | P-Value |
| Estimated blood loss, ml/kg, median (IQR) | 17.14 (8.73) | 17.71 (8.38) | 18.14 (8.73) | 0.910 |
| Urine volume, ml/kg/h, median (IQR) | 2.88 (2.20) | 3.83 (3.74) | 2.79 (2.01) | 0.061 |
| Isotonic crystalloid transfusion, ml/kg/h, median (IQR) | 5.88 (2.65) | 4.70 (2.97) | 5.92 (2.52) | 0.182 |
| Hydroxyethyl starch 130/0.4, ml/kg/h, median (IQR) | 2.50 (1.23) | 2.34 (1.41) | 2.50 (1.27) | 0.643 |
| RBC transfusion, mL/kg, median (IQR) | 10.00 (8.99) | 13.33 (14.64) | 10.00 (7.89) | 0.103 |
| Surgical duration, minutes, median (IQR) | 262.0 (130) | 282.5 (173) | 262.0 (128) | 0.579 |
| Anesthesia time, minute, median (IQR) | 330 (132.5) | 365 (215.0) | 330 (120.0) | 0.390 |
| Hemoglobin, g/l, median (IQR) | 127 (31.00) | 114 (34.25) | 128 (31.00) | 0.133 |
| Albumin, mg, median (IQR) | 39.0 (7.40) | 36.2 (8.33) | 39.4 (7.35) | 0.107 |
| Prealbumin, mg/l, median (IQR) | 221.0 (116) | 195.5 (112) | 228.0 (116) | 0.400 |
| APTT, seconds, median (IQR) | 25.80 (3.00) | 26.65 (4.60) | 25.70(4.25) | 0.658 |
| FIB, g/l, median (IQR) | 2.79 (1.30) | 2.16 (1.97) | 2.87 (1.21) | 0.108 |
| INR, score, median (IQR) | 0.97 (0.09) | 1.02 (0.16) | 0.97 (0.08) | 0.380 |
| PT, seconds, median (IQR) | 11.40 (1.0) | 11.65 (1.7) | 11.40 (0.9) | 0.495 |
| TT, seconds, mean (SD) | 16.68 (1.37) | 16.64 (1.14) | 16.68 (1.40) | 0.929 |
| SCr, μmol/l, mean (SD) | 56.79 (12.95) | 56.17 (16.51) | 56.86 (12.59) | 0.861 |
| BUN, mmol/l, median (IQR) | 5.86 (2.46) | 5.60 (2.69) | 5.97 (2.51) | 0.808 |
| Second surgery, number (%) | 26 (21.5) | 8 (66.7) | 18 (16.5) | 0.000 |
| Hospital stay, days, median (IQR) | 19 (16) | 35 (34) | 18 (14) | 0.005 |
| Admission to ICU, number (%) | 13 (10.7) | 4 (33.3) | 9 (8.3) | 0.025 |
According to the univariate analysis, the infection group had significantly more postoperative admissions to the Intensive Care Unit (ICU), second surgeries and prolonged hospital stays than the non-infection group (P < 0.05) (Table 2).
3.3 Multivariate analysis of risk factors for SSI
During binary logistic regression analysis, the occurrence of postoperative SSI (infection = 1, non-infection = 0) was used as the dependent variable. Independent variables included ASA grade, type 2 diabetes, urine volume, second surgery, postoperative admission to ICU, and hospital stay, with values assigned to urine volume (≥3.49 ml/mg/h = 1, <3.49 ml/mg/h = 0) and hospitalization time (≥26.5 days = 1, <26.5 days = 0). In the case of massive hemorrhages, ASA grade, type 2 diabetes, and urine volume were found to be independently risk factors for SSI.(P < 0.05) (Table 3 and Fig. 2). We conducted a multivariate analysis of independent risk factors for SSI and comorbidities. The results are as follows: the relative weight of hypertension is 0.005, coronary heart disease is 0.003, COPD is 0.003, old cerebral infarction is 0.014, type 2 diabetes is 0.089, urine volume is 0.046, ASA is 0.098, admission to ICU is 0.004, hospital stay is 0.006, and second surgery is 0.014 (Table 4).
| Variable | Odds ratio | 95 % CI | P-Value |
| ASA grade | 19.57 | 1.88–203.40 | 0.013 |
| Urine volume | 5.79 | 1.05–31.93 | 0.044 |
| Type 2 diabetes | 19.92 | 1.78–223.42 | 0.015 |
| Hospital stay | 1.51 | 0.14–16.75 | 0.739 |
| Admission to ICU | 2.57 | 0.35–18.79 | 0.353 |
| Second surgery | 3.86 | 0.35–42.71 | 0.271 |
| Variable | P-Value | Relative weights |
| Hypertension | 0.475 | 0.005 |
| Coronary heart disease | 0.589 | 0.003 |
| COPD | 0.571 | 0.003 |
| Old cerebral infarction | 0.214 | 0.014 |
| Type 2 diabetes | 0.001 | 0.089 |
| Urine volume | 0.023 | 0.046 |
| ASA | 0.001 | 0.098 |
| Admission to ICU | 0.534 | 0.004 |
| Hospital stay | 0.431 | 0.006 |
| Second surgery | 0.217 | 0.014 |
3.4 Construction of the SSI risk prediction model
According to the logistic regression analysis, the following risk prediction equation was formulated: Logit(P) = −6.947 + 3.198 ∗ ASA grade + 2.067 ∗ urine volume + 3.572 ∗ type 2 diabetes. The goodness of fit of the regression equation was assessed using the Hosmer-Lemeshow test (P = 0.246). A nomogram risk model for SSI was established based on the variables (history of type 2 diabetes, ASA grade, and urine volume) selected by the risk score (Fig. 3).14,15 ROC curve analysis revealed that the risk score to predict SSI after massive hemorrhage had an area under the curve of 0.916 (Figs. 3 and 95 % CI: 0.849–0.982, P < 0.001), indicating a strong predictive ability. Moreover, the comparison revealed that patients with type 2 diabetes, urine volume ≥3.49 ml/kg/h, and high ASA grade had significantly higher risk scores than the non-infection group (P < 0.05, Fig. 4). Bootstrap resampling was carried out, and after 1000 resampling repetitions, a calibrated curve was obtained (Fig. 5). The curve closely aligned with the diagonal line, signifying that the predicted risk closely matched the actual risk, and the model demonstrated excellent predictive performance.
4 Discussion
Postoperative SSI constitutes a prevalent complication of surgery, often associated with high morbidity and mortality rates. It is now understood that SSI not only hinders patients' clinical recovery but can also undermine the benefits of surgery.16–18 Clinical investigations have consistently underscored the relatively high prevalence of hospital-acquired infections among surgically hospitalized patients. Without timely intervention, worsening infectious symptoms in patients can escalate into systemic infections.19,20
Notwithstanding that significant scientific progress has been achieved in recent years, no consensus has been reached concerning SSI following massive hemorrhage. Researchers investigated the risk factors for SSI in massive hemorrhage patients. Based on clinical data from 121 patients, univariate analysis unveiled several potential risk factors, including ASA grade, type 2 diabetes, urine volume, repeat surgery postoperative ICU admission, and length of hospital stay. Furthermore, logistic regression analysis results identified ASA grade, type 2 diabetes, and urine volume as independent risk factors for SSI. Moreover, the results of multivariate analysis showed the relative weights of various risk factors.
We devised a predictive model that incorporated the aforementioned independent risk factors related to SSI after massive hemorrhage. ROC and calibration curves were generated to assess the model's performance. An AUC of 0.916 was obtained, indicating a high level of predictive accuracy. Besides, the model exhibited good calibration.
The ASA grade, designed to assess patients' physical condition and overall health for surgery and anesthesia risk evaluation,21,22 has been shown to influence postoperative mortality and become a risk factor for SSI after surgery.23,24 Patients with higher ASA grades generally present more comorbidities and reduced organ compensatory abilities. Consequently, they are at increased risk for anesthesia complications and infection following massive bleeding. Significant blood loss during surgery can lead to decreased blood volume, reducing resistance post-surgery and precipitating the onset of SSI. While the ASA grade is a useful tool, its assessment can indeed be subjective for several reasons: Inter-Rater Reliability: Different clinicians might have varying interpretations of what constitutes "mild" or "severe" systemic disease. Lack of Specific Criteria: The ASA classification system is broad and does not provide specific criteria for each grade, leading to potential inconsistencies in its application. Dynamic Nature of Health Status: A patient's health status can change over time, and the ASA grade might not always accurately reflect their current health at the time of surgery. To mitigate the subjectivity in ASA grade assessment, we adopted several strategies. Standardized Training: Ensuring that all clinicians involved in preoperative assessments are trained in the use of the ASA classification system. Clear Documentation: Encouraging detailed documentation of the reasons for assigning a specific ASA grade. Regular Review: Periodically reviewing the use of the ASA classification system and its impact on patient outcomes to identify areas for improvement. In conclusion, while the ASA grade is a valuable predictor of SSI, its subjective nature should be acknowledged and managed to ensure its utility in clinical practice. Thus, for patients with complex medical histories and multiple comorbidities, comprehensive preoperative assessments, optimized nutritional support before surgery, enhancement of bodily functions, and minimization of intraoperative blood loss based on the relative weights of various risk factors and models are all strategies hat can help reduce the risk of postoperative SSI. Actively treat patients' comorbidities during the perioperative period, reduce recurrence, and keep the body in a relatively good state to prepare for surgery. Actively control blood sugar, shorten hospitalization time, and reduce the risk of infection. Patients with severe bleeding should maintain circulating blood volume during surgery while actively utilizing auxiliary tools such as ultrasound to assess blood volume in real-time, prevent tissue edema caused by volume overload, control urine output, and prevent infection.
Our findings underscore the increased risk of SSI among diabetic patients. Some diabetic patients exhibit inadequate blood glucose control, fostering an environment rich in nutrients for microbial proliferation at the surgical site.8,25 Furthermore, diabetic patients often experience compromised immune function, leading to delayed wound healing and an elevated susceptibility to SSI.1,26 According to the clinical practice guidelines for perioperative blood glucose management, we conducted perioperative blood glucose management.27 Consequently, providing efficient treatment throughout the care process to ensure effectiveness, expedite recovery, and reduce hospitalization duration is paramount. Healthcare professionals should also maintain strict aseptic measures during surgery, regulate operating room personnel, and minimize the risk of patient infections.
In cases of massive hemorrhage, fluid resuscitation, and transfusions are common strategies employed to stabilize the microcirculation.28 Swift fluid resuscitation is essential for replenishing circulating blood volume and sustaining tissue perfusion.29,30 Nonetheless, while fluid resuscitation is a cornerstone of massive hemorrhage management, it is not without potential drawbacks. Fluid overload presents a significant concern and can lead to tissue edema.31 Excessive fluid administration can result in fluid extravasation into interstitial spaces, compromising tissue oxygenation and organ function.32,33 Additionally, fluid overload can negatively affect kidney function, increasing urine volume. This phenomenon arises from multiple factors, including heightened hydrostatic pressure in renal tubules due to excessive fluid volume, which promotes diuresis.33 Furthermore, the release of atrial natriuretic peptide can further augment urine excretion.34 In our study, urine volume ≥3.49 ml/kg/h was an independent risk factor for SSI after massive hemorrhage. An increase in urine output indicates excessive perfusion, leading to tissue edema and subsequent ischemia-reperfusion injury due to excessive volume load. During ischemia, the antioxidant defense mechanism of cells is inhibited, leading to an increase in reactive oxygen species (ROS) and reactive nitrogen species (RNS) produced during reperfusion. These free radicals can damage proteins, lipids, and DNA, causing cellular dysfunction and tissue damage.35 Ischemia reperfusion can activate inflammatory responses, leading to the release of cytokines and chemokines, and promoting leukocyte infiltration into the damaged area. These white blood cells produce a large amount of oxygen free radicals through respiratory burst, further exacerbating tissue damage.36 Inflammatory reactions and cellular damage can lead to microvascular dysfunction, including impaired vasoconstriction, vasodilation, and increased vascular permeability. These changes can result in inadequate tissue perfusion, further exacerbating damage and increasing the risk of infection.37
Understanding the implications of fluid overload, tissue edema, and heightened urine volume is vital in the management of patients with massive hemorrhage. While fluid resuscitation remains a critical intervention, achieving a balance between restoring vascular volume and preventing fluid overload is crucial for optimizing patient outcomes. According to the perioperative fluid management for major elective surgery, we conducted perioperative fluid management.38
While this study successfully established a predictive model for SSI after massive hemorrhage, it is not without limitations. It remains a single-center, small-sample analysis, potentially introducing data bias. Further research involving large-sample, multi-center studies is warranted to collect more comprehensive and reliable data support.
5 Conclusion
In summary, this retrospective study identified several risk factors for SSI following massive hemorrhage. ASA grade, type 2 diabetes, and urine volume were identified as independent predictive factors for SSI. We harnessed these three indicators to establish a predictive model with an AUC of 0.916, indicating strong clinical utility. By maintaining precise fluid management and effective glycemic control, the incidence of SSI can be reduced. Targeting high-risk patients based on ASA grade, diabetes status, and urine volume may optimize resource allocation and clinical outcomes. With multifaceted preventive strategies tailored to individual patient risk profiles, the burden of SSI after a massive hemorrhage can be mitigated.
Funding
This study was supported by the Medical Science Research Project Plan of the Hebei Provincial Health Commission (No. 20240666).
Declaration of patient consent form
No applicable.
Ethical approval
The study was approval by Ethics Committee of this institution.
Ethical statement
This study received approval from the Ethics Committee of Hebei Province Cangzhou Hospital of Integrated Traditional and Western Medicine.
Patient's consent
This study is a retrospective study. No patient consent was required.
CRediT authorship contribution statement
Wei-Chao Zheng: Data curation, Project administration, Writing – original draft. Yang Bai: Data curation, Writing – original draft. Jian-Lei Ge: Data curation, Writing – original draft. Lei-Shuai Lv: Methodology, Formal analysis, Writing – original draft. Bin Zhao: Writing – original draft. Hong-Li Wang: Methodology, Formal analysis. Li-Min Zhang: Conceptualization, Project administration, Writing – review & editing, Reviewing, Writing – review & editing, Supervision.
References
- Risk factors for surgical site infection after spinal surgery: a meta-analysis. World neurosurgery. 2016;95:507-515.
- [Google Scholar]
- Prosthetic joint infections and legal disputes: a threat to the future of prosthetic orthopedics. J Orthop Traumatol : official journal of the Italian Society of Orthopaedics and Traumatology. 2021;22:44.
- [Google Scholar]
- C-reactive protein (CRP)/albumin-to-globulin ratio (AGR) is a valuable test for diagnosing periprosthetic joint infection: a single-center retrospective study. J Orthop Traumatol : official journal of the Italian Society of Orthopaedics and Traumatology. 2022;23:36.
- [Google Scholar]
- High contribution and impact of resistant gram negative pathogens causing surgical site infections at a multi-hospital healthcare system in Saudi Arabia, 2007-2016. BMC Infect Dis. 2020;20:275.
- [Google Scholar]
- Surgical site infection following fixation of acetabular fractures. Hip & pelvis. 2017;29:176-181.
- [Google Scholar]
- Risk factors for surgical site infection following operative treatment of ankle fractures: a systematic review and meta-analysis. Int J Surg. 2018;56:124-132.
- [Google Scholar]
- A nomogram for accurately predicting the surgical site infection following transforaminal lumbar interbody fusion in type 2 diabetes patients, based on glycemic variability. Int Wound J. 2023;20:981-994.
- [Google Scholar]
- Pathophysiology of hemorrhagic shock. Journal of veterinary emergency and critical care (San Antonio, Tex : 2001). 2022;32:22-31.
- [Google Scholar]
- Resuscitation with Hydroxyethyl starch maintains hemodynamic coherence in ovine hemorrhagic shock. Anesthesiology. 2020;132:131-139.
- [Google Scholar]
- A systematic review of large animal models of combined traumatic brain injury and hemorrhagic shock. Neurosci Biobehav Rev. 2019;104:160-177.
- [Google Scholar]
- Incidence of surgical site infection after craniotomy: comparison between three months and twelve months of epidemiological surveillance. Braz J Infect Dis : an official publication of the Brazilian Society of Infectious Diseases. 2018;22:433-437.
- [Google Scholar]
- Health care-associated infections studies project: an American journal of infection control and national healthcare safety Network data quality collaboration case study - chapter 9 surgical site infection event (SSI) case study. American journal of infection control. 2022;50:799-800.
- [Google Scholar]
- Predicting medication nonadherence risk in a Chinese inflammatory rheumatic disease population: development and assessment of a new predictive nomogram. Patient Prefer Adherence. 2018;12:1757-1765.
- [Google Scholar]
- Beliefs about medicines and non-adherence in patients with stroke, diabetes mellitus and rheumatoid arthritis: a cross-sectional study in China. BMJ Open. 2017;7
- [Google Scholar]
- Prophylactic negative-pressure wound therapy prevents surgical site infection in abdominal surgery: an updated systematic review and meta-analysis of randomized controlled trials and observational studies. Clin Infect Dis : an official publication of the Infectious Diseases Society of America. 2021;73:e3804-e3813.
- [Google Scholar]
- Incidence of infection in non-tunnelled thoracic epidural catheters after major abdominal surgery. Acta Anaesthesiol Scand. 2020;64:1312-1318.
- [Google Scholar]
- Comparison of mortality rate and septic and aseptic revisions in total hip arthroplasties for osteoarthritis and femoral neck fracture: an analysis of the German Arthroplasty Registry. J Orthop Traumatol : official journal of the Italian Society of Orthopaedics and Traumatology. 2023;24:29.
- [Google Scholar]
- Results of cryopreserved arterial allograft replacement for thoracic and thoracoabdominal aortic infections. J Vasc Surg. 2021;73:626-634.
- [Google Scholar]
- Surgical wound infection prevention using topical negative pressure therapy on closed abdominal incisions - the 'SWIPE IT' randomized clinical trial. J Hosp Infect. 2021;110:76-83.
- [Google Scholar]
- Hierarchical regression of ASA prediction model in predicting mortality prior to performing emergency laparotomy a systematic review. Annals of medicine and surgery. 2020;60:743-749.
- [Google Scholar]
- ASA class is a reliable independent predictor of medical complications and mortality following surgery. Int J Surg. 2015;18:184-190.
- [Google Scholar]
- Utilization of the American Society of Anesthesiologists (ASA) classification system in evaluating outcomes and costs following deformity spine procedures. Spine deformity. 2021;9:185-190.
- [Google Scholar]
- The ASA score predicts infections, cardiovascular complications, and hospital readmissions after hip fracture - a nationwide cohort study. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2021;32:2185-2192.
- [Google Scholar]
- Prevalence of surgical site infection and risk factors in patients after knee surgery: a systematic review and meta-analysis. Int Wound J. 2024;21
- [Google Scholar]
- Diabetes and risk of surgical site infection: a systematic review and meta-analysis. Infection control and hospital epidemiology. 2016;37:88-99.
- [Google Scholar]
- Chinese clinical practice guidelines for perioperative blood glucose management. Diabetes/metabolism research and reviews. 2021;37
- [Google Scholar]
- Association between intraoperative fluid overload and postoperative debridement in major sacrum tumor resection: a propensity score matching study. Medicine. 2022;101
- [Google Scholar]
- Perioperative fluid management and volume assessment. Anesthesiol Clin. 2023;41:191-209.
- [Google Scholar]
- Effects of acute plasma volume expansion on renal perfusion, filtration, and oxygenation after cardiac surgery: a randomized study on crystalloid vs colloid. British journal of anaesthesia. 2015;115:736-742.
- [Google Scholar]
- Protective renal effects of atrial natriuretic peptide: where are we now? Front Physiol. 2021;12
- [Google Scholar]
- Unveiling the crucial roles of O(2)(•-) and ATP in hepatic ischemia-reperfusion injury using dual-color/reversible fluorescence imaging. J Am Chem Soc. 2023;145:19662-19675.
- [Google Scholar]
- Ischemia-reperfusion injury: molecular mechanisms and therapeutic targets. Signal Transduct Targeted Ther. 2024;9:12.
- [Google Scholar]
- Current mechanistic concepts in ischemia and reperfusion injury. Cell Physiol Biochem : international journal of experimental cellular physiology, biochemistry, and pharmacology. 2018;46:1650-1667.
- [Google Scholar]
- Perioperative fluid management for major elective surgery. Br J Surg. 2020;107:e56-e62.
- [Google Scholar]