Prediction tool for high risk of surgical site infection in spinal surgery

Namba, Takanori; Ueno, Masaki; Inoue, Gen; Imura, Takayuki; Saito, Wataru; Nakazawa, Takahiro; Miyagi, Masayuki; Shirasawa, Eiki; Takahashi, Osamu; Takaso, Masashi

doi:10.1017/ice.2020.107

Cited by 9 publications

(8 citation statements)

References 30 publications

(25 reference statements)

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“…As in spinal surgery the incidence of infection is higher than in general orthopedic operations, the HFRS can be a simple predictor. Recently, a scoring system was published with the single aim to identify the risk patients to reduce complex and expensive infection treatments and thereby reducing costs in the healthcare system [28].…”

Section: Discussionmentioning

confidence: 99%

Hospital frailty risk score predicts adverse events in spine surgery

et al. 2022

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Purpose The Hospital Frailty Risk Score (HFRS) is derived from routinely collected data and validated as a geriatric risk stratification tool. This study aimed to evaluate the utility of the HFRS as a predictor for postoperative adverse events in spine surgery. Methods In this retrospective analysis of 2042 patients undergoing spine surgery at a university spine center between 2011 and 2019, HFRS was calculated for each patient. Multivariable logistic regression models were used to assess the relationship between the HFRS and postoperative adverse events. Adverse events were compared between patients with high or low frailty risk. Results Patients with intermediate or high frailty risk showed a higher rate of reoperation (19.7% vs. 12.2%, p < 0.01), surgical site infection (3.4% vs. 0.4%, p < 0.001), internal complications (4.1% vs. 1.1%, p < 0.01), Clavien–Dindo IV complications (8.8% vs. 3.4%, p < 0.001) and transfusion (10.9% vs. 1.5%, p < 0.001). Multivariable logistic regression analyses revealed a high HFRS as independent risk factor for reoperation [odds ratio (OR) = 1.1; 95% confidence interval (CI) 1.0–1.2], transfusion (OR = 1.3; 95% CI 1.2–1.4), internal complications (OR = 1.2; 95% CI 1.1–1.3), surgical site infections (OR = 1.3; 95% CI 1.2–1.5) and other complications (OR = 1.3; 95% CI 1.2–1.4). Conclusion The HFRS can predict adverse events and is an easy instrument, fed from routine hospital data. By identifying risk patients at an early stage, the individual patient risk could be minimized, which leads to less complications and lower costs. Level of evidence Level III – retrospective cohort study Trial registration The study was approved by the local ethics committee (20-1821-104) of the University of Regensburg in February 2020.

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Section: Discussionmentioning

confidence: 99%

Hospital frailty risk score predicts adverse events in spine surgery

et al. 2022

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“…The results of He et al [ 27 ] supported our opinion, with the multivariable logistic regression analysis showing an albumin level of <35 g/L being a risk factor for post-operative surgical site infection [ 27 ]. A study on risk factors for surgical infection in 824 patients showed that albumin value <32 g/L could predict the incidence of infection after spinal surgery [ 28 ]. Tuberculosis causes poor appetite and insufficient nutritional intake in patients, resulting in low albumin.…”

Section: Discussionmentioning

confidence: 99%

Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram

Chen

Liu

et al. 2022

Surgical Infections

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Background: The purpose of this study was to predict the surgical site infection risk after spinal tuberculosis surgery based on a nomogram. Patients and Methods: We collected the clinical data of patients who underwent spinal tuberculosis surgery in our hospital and included all the data in the least absolute shrinkage and selection operator (LASSO) regression analysis. Next, the selected parameters were analyzed using logistic regression. The logistic regression analysis and receiver operating characteristic (ROC) curve analysis were further used to obtain statistically significant parameters. These parameters were then used to construct a nomogram. The C-index, ROC curve, and decision curve analysis (DCA) were used to assess the predictive ability and accuracy of the nomogram, whereas internal verification was used to calculate the C-index by bootstrapping with 1,000 resamples. Results: A total of 394 patients with spinal tuberculosis surgery were included in the study, of whom 76 patients had surgical site infections whereas 318 patients did not. The predicted risk of surgical site infection in the nomogram ranged between 0.01 and 0.98. Both the value of the C-index of the nomogram (95% confidence interval [CI], 0.62–0.76) and the area under the curve (AUC) were found to be 0.69. The net benefit of the model ranged between 0.01 and 0.99. In contrast, the C-index calculated by the internal verification method of the nomogram was found to be 0.68. Conclusions: The risk factors predicting surgical site infection after spinal tuberculosis surgery included albumin, lesion segment, operation time, and incision length.

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“…However, the majority of studies have developed tools for patients undergoing nongynecologic abdominal surgery, neurologic surgery, and orthopedic surgery, with no tools designed specifically for urogynecologic or general gynecologic surgery. [22][23][24][25][26][27] Although generic SSI prediction models such as the American College of Surgeons National…”

Section: Discussionmentioning

confidence: 99%

“…There has been increasing attention and interest in developing and applying prediction algorithms in various clinical settings; given that SSI is potentially preventable, many researchers have focused research on this outcome. However, the majority of studies have developed tools for patients undergoing nongynecologic abdominal surgery, neurologic surgery, and orthopedic surgery, with no tools designed specifically for urogynecologic or general gynecologic surgery 22–27 . Although generic SSI prediction models such as the American College of Surgeons National Surgical Quality Improvement Program calculator exist, when they are applied to specific nongeneral surgical procedures, they tend to have poor discriminatory ability 8,28,29 .…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Model for Predicting Surgical Site Infection After Pelvic Organ Prolapse Surgery

Sheyn

Gregory

Osazuwa‐Peters³

et al. 2022

Female Pelvic Med Reconstr Surg

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ImportanceSurgical site infection (SSI) is a common and costly complication. Targeted interventions in high-risk patients may lead to a reduction in SSI; at present, there is no method to consistently identify patients at increased risk of SSI.ObjectiveThe aim of this study was to develop and validate a model for predicting risk of SSI after pelvic organ prolapse surgery.Study DesignWomen undergoing surgery between 2011 and 2017 were identified using Current Procedural Terminology codes from the Centers for Medicare and Medicaid Services 5% Limited Data Set. Surgical site infection ≤90 days of surgery was the primary outcome, with 41 candidate predictors identified, including demographics, comorbidities, and perioperative variables. Generalized linear regression was used to fit a full specified model, including all predictors and a reduced penalized model approximating the full model. Model performance was measured using the c-statistic, Brier score, and calibration curves. Accuracy measures were internally validated using bootstrapping to correct for bias and overfitting. Decision curves were used to determine the net benefit of using the model.ResultsOf 12,334 women, 4.7% experienced SSI. The approximated model included 10 predictors. Model accuracy was acceptable (bias-corrected c-statistic [95% confidence interval], 0.603 [0.578–0.624]; Brier score, 0.045). The model was moderately calibrated when predicting up to 5–6 times the average risk of SSI between 0 and 25–30%. There was a net benefit for clinical use when risk thresholds for intervention were between 3% and 12%.ConclusionsThis model provides estimates of probability of SSI within 90 days after pelvic organ prolapse surgery and demonstrates net benefit when considering prevention strategies to reduce SSI.

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Prediction tool for high risk of surgical site infection in spinal surgery

Cited by 9 publications

References 30 publications

Hospital frailty risk score predicts adverse events in spine surgery

Hospital frailty risk score predicts adverse events in spine surgery

Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram

Development and Validation of a Model for Predicting Surgical Site Infection After Pelvic Organ Prolapse Surgery

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