Preoperative Urinary Tract Infection Increases Postoperative Morbidity in Spine Patients

A retrospective analysis of the incidence and risk factors of perioperative urinary tract infections after total hysterectomy

INTRODUCTION

Perioperative urinary tract infections (PUTIs) are common in the United States and are a significant contributor to high healthcare costs. There is a lack of large studies on the risk factors for PUTIs after total hysterectomy (TH).

METHODS

We conducted a retrospective study using a national inpatient sample (NIS) of 445,380 patients from 2010 to 2019 to analyze the risk factors and annual incidence of PUTIs associated with TH perioperatively.

RESULTS

PUTIs were found in 9087 patients overall, showing a 2.0% incidence. There were substantial differences in the incidence of PUTIs based on age group (P < 0.001). Between the two groups, there was consistently a significant difference in the type of insurance, hospital location, hospital bed size, and hospital type (P < 0.001). Patients with PUTIs exhibited a significantly higher number of comorbidities (P < 0.001). Unsurprisingly, patients with PUTIs had a longer median length of stay (5 days vs. 2 days; P < 0.001) and a higher in-hospital death rate (from 0.1 to 1.1%; P < 0.001). Thus, the overall hospitalization expenditures increased by $27,500 in the median ($60,426 vs. $32,926, P < 0.001) as PUTIs increased medical costs. Elective hospitalizations are less common in patients with PUTIs (66.8% vs. 87.6%; P < 0.001). According to multivariate logistic regression study, the following were risk variables for PUTIs following TH: over 45 years old; number of comorbidities (≥ 1); bed size of hospital (medium, large); teaching hospital; region of hospital(south, west); preoperative comorbidities (alcohol abuse, deficiency anemia, chronic blood loss anemia, congestive heart failure, diabetes, drug abuse, hypertension, hypothyroidism, lymphoma, fluid and electrolyte disorders, metastatic cancer, other neurological disorders, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, solid tumor without metastasis, valvular disease, weight loss); and complications (sepsis, acute myocardial infarction, deep vein thrombosis, gastrointestinal hemorrhage, pneumonia, stroke, wound infection, wound rupture, hemorrhage, pulmonary embolism, blood transfusion, postoperative delirium).

CONCLUSIONS

The findings suggest that identifying these risk factors can lead to improved preventive strategies and management of PUTIs in TH patients. Counseling should be done prior to surgery to reduce the incidence of PUTIs.

THE MANUSCRIPT ADDS TO CURRENT KNOWLEDGE

In medical practice, the identification of risk factors can lead to improved patient prevention and treatment strategies. We conducted a retrospective study using a national inpatient sample (NIS) of 445,380 patients from 2010 to 2019 to analyze the risk factors and annual incidence of PUTIs associated with TH perioperatively. PUTIs were found in 9087 patients overall, showing a 2.0% incidence. We found that noted increased length of hospital stay, medical cost, number of pre-existing comorbidities, size of the hospital, teaching hospitals, and region to also a play a role in the risk of UTI's.

CLINICAL TOPICS

Urogynecology.

Prophylactic alpha blockers fail to prevent postoperative urinary retention following orthopaedic procedures: evidence from a meta-analysis and trial sequential analysis of comparative studies

Objective: The present systematic review and meta-analysis aimed to estimate the prophylactic effect of alpha blockers against postoperative urinary retention (POUR) in orthopaedic patients. Methods: PubMed, Embase, Web of Science and Cochrane Library databases were searched between 1 January 1990 and 1 March 2023. The studies reporting the preventive efficacy of alpha blockers on POUR after orthopaedic procedures were identified. The pooled rates of POUR in the Intervention group (patients receiving alpha blockers) and the Control group (patients not receiving alpha blockers) were estimated and compared. The risk ratios (RRs) were calculated using the random-effects model. Subgroup analysis was performed based on surgical type. Trial sequential analysis (TSA) was conducted to confirm the robustness of pooled results. Results: Seven studies containing 1,607 patients were identified. The rates of POUR were similar between the two groups (Intervention group: 126/748 [16.8%] VS. Control group: 168/859 [19.6%]; RR = 0.75; 95% confidence interval [CI] 0.51 to 1.09; p = 0.130; Heterogeneity: I2 = 67.1%; p = 0.006). No significant difference in the incidence of POUR was observed in either the Arthroplasty subgroup or Spine surgery subgroup. The result of TSA suggested that the total sample size of the existing evidence might be insufficient to draw conclusive results. Administrating alpha blockers was associated with a higher risk of complications (88/651 [13.5%] VS. 56/766 [7.3%]; RR = 1.73; 95% CI 1.27 to 2.37; p = 0.0005; Heterogeneity: I2 = 0%; p = 0.69). Conclusion: Prophylactic alpha blockers do not reduce the risk of POUR in orthopaedic procedures, and administrating these drugs was associated with a higher risk of complications. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=409388.

Using multiple indicators to predict the risk of surgical site infection after ORIF of tibia fractures: a machine learning based study

Objective

Surgical site infection (SSI) are a serious complication that can occur after open reduction and internal fixation (ORIF) of tibial fractures, leading to severe consequences. This study aimed to develop a machine learning (ML)-based predictive model to screen high-risk patients of SSI following ORIF of tibial fractures, thereby aiding in personalized prevention and treatment.

Methods

Patients who underwent ORIF of tibial fractures between January 2018 and October 2022 at the Department of Emergency Trauma Surgery at Ganzhou People's Hospital were retrospectively included. The demographic characteristics, surgery-related variables and laboratory indicators of patients were collected in the inpatient electronic medical records. Ten different machine learning algorithms were employed to develop the prediction model, and the performance of the models was evaluated to select the best predictive model. Ten-fold cross validation for the training set and ROC curves for the test set were used to evaluate model performance. The decision curve and calibration curve analysis were used to verify the clinical value of the model, and the relative importance of features in the model was analyzed.

Results

A total of 351 patients who underwent ORIF of tibia fractures were included in this study, among whom 51 (14.53%) had SSI and 300 (85.47%) did not. Of the patients with SSI, 15 cases were of deep infection, and 36 cases were of superficial infection. Given the initial parameters, the ET, LR and RF are the top three algorithms with excellent performance. Ten-fold cross-validation on the training set and ROC curves on the test set revealed that the ET model had the best performance, with AUC values of 0.853 and 0.866, respectively. The decision curve analysis and calibration curves also showed that the ET model had the best clinical utility. Finally, the performance of the ET model was further tested, and the relative importance of features in the model was analyzed.

Conclusion

In this study, we constructed a multivariate prediction model for SSI after ORIF of tibial fracture through ML, and the strength of this study was the use of multiple indicators to establish an infection prediction model, which can better reflect the real situation of patients, and the model show great clinical prediction performance.

Factors Affecting the Waiting Time from Injury to Surgery in Elderly Patients with a Cervical Spine Injury: A Japanese Multicenter Survey

OBJECTIVE

The management of cervical spine injuries in the elderly is often complicated by the presence of multiple medical comorbidities, and it is not uncommon for preoperative testing to reveal other conditions that require the postponement of surgery. However, the factors that affect the waiting time from injury to surgery have not been clarified. The purpose of this multicenter database study was to analyze the clinical features and identify the factors affecting the number of days waited between injury and surgery in elderly patients with a cervical spine injury.

METHODS

We retrospectively reviewed the case histories of 1512 Japanese patients with a cervical spinal injury, who were seen at 33 institutions. After excluding patients who were not initially receiving a surgery for cervical spinal injury, 694 patients were ultimately analyzed. Based on a multivariate mixed model, we determined the factors related to the number of days from injury to surgery.

RESULTS

The mean time from injury to surgery was 12.3 days. Multivariate analysis revealed delays of 10.7 days for a renal disorder, 7.3 days for anticoagulant use, and 15.2 days for non-surgical thoracolumbar fracture as factors prolonging wait time. In contrast, a C3 or lower spine injury was significantly associated with a shortening of 9.5 days to surgery.

CONCLUSIONS

This multicenter database study identified several factors influencing the time between injury and cervical spine surgery in elderly patients. While renal impairment, anticoagulant use, and non-surgical thoracolumbar fracture may increase the number of days to surgery, trauma to C3 or lower may expedite surgical treatment.