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Unique predictors and economic burden of superficial and deep/organ space surgical site infections following pancreatectomy.

ABSTRACT: BACKGROUND:Surgical site infections (SSIs) are common following pancreatectomy and associated with significant morbidity and economic burden. We sought to identify distinct predictors for superficial versus deep/organ space SSIs and their effects on surgical outcomes. METHODS:ACS-NSQIP targeted pancreatectomy 2014 and 2015 databases were queried. Univariate and multivariate models were developed for both types of SSI, length of stay (LOS), and readmission. Costs were estimated based on Centers for Medicare & Medicaid Services (CMS) recommendations. RESULTS:Of 8093 patients, there were 422 (5.2%) superficial and 1005 (12.4%) deep/organ space SSIs. On multivariate analyses, preoperative biliary stenting was predictive only for superficial SSI (OR: 2.21), while BMI of 25-29.9 (OR: 1.25) and BMI ?30 kg/m2 (OR: 1.53), pancreatic duct size <3 mm (OR: 1.30), and intermediate (OR: 1.67) versus hard gland texture were predictors of deep/organ-space SSI. Superficial and deep/organ space SSIs were independent predictors of prolonged LOS (OR: 1.74 vs 1.80) and readmission (OR: 2.59 vs 6.57). Additional readmission costs per patient secondary to superficial SSI and deep/organ space SSI were $7661.37 and $18,409.42, respectively. CONCLUSION:Deep/organ space SSI contributes more profoundly to prolonged hospital stay, readmission, and additional costs, suggesting that strategies should focus on preferential prevention of deep/organ space infections.

SUBMITTER: Fadayomi AB

PROVIDER: S-EPMC6046258 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Unique predictors and economic burden of superficial and deep/organ space surgical site infections following pancreatectomy.

Fadayomi Ayòtúndé B AB Kasumova Gyulnara G GG Tabatabaie Omidreza O de Geus Susanna W L SWL Kent Tara S TS Ng Sing Chau SC Moser A James AJ Callery Mark P MP Ashley Stanley W SW Tseng Jennifer F JF

HPB : the official journal of the International Hepato Pancreato Biliary Association 20180309 7

<h4>Background</h4>Surgical site infections (SSIs) are common following pancreatectomy and associated with significant morbidity and economic burden. We sought to identify distinct predictors for superficial versus deep/organ space SSIs and their effects on surgical outcomes.<h4>Methods</h4>ACS-NSQIP targeted pancreatectomy 2014 and 2015 databases were queried. Univariate and multivariate models were developed for both types of SSI, length of stay (LOS), and readmission. Costs were estimated bas ...[more]

PMID: 29526467

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Project description:BACKGROUND:Organ/space surgical site infection (OS-SSI) develops frequently after trauma laparotomies and is associated with significant morbidity. No valid model exists to accurately risk-stratify the probability of OS-SSI development after emergent laparotomy. Risk stratification for OS-SSI in these patients could guide promising, but unproven, interventions for OS-SSI prevention, such as more frequent dosing of intraoperative antibiotics or direct peritoneal resuscitation. We hypothesize that in trauma patients who undergo emergent laparotomy, probability of OS-SSI can be accurately estimated using patient data available during the index operation. METHODS:Retrospective review was performed on a prospectively maintained database of emergent trauma laparotomies from 2011 to 2016. Patient demographics and risk factors for OS-SSI were collected. We performed Bayesian multilevel logistic regression to develop the model based on a 70% training sample. Evaluation of model fit using area under the curve was performed on a 30% test sample. The Bayesian approach allowed the model to address clustering of patients within physician while implementing regularization to improve predictive performance on test data. RESULTS:One hundred seventy-two (15%) of 1,171 patients who underwent laparotomy developed OS-SSI. Variables thought to affect development of surgical site infections and were available to the surgeon near the conclusion of the trauma laparotomy were included in the model. Two variables that contributed most to OS-SSIs were damage-control laparotomy and colon resection. The area under the curve of the predictive model validated on the test sample was 0.78 (95% confidence interval, 0.71-0.85). CONCLUSION:Using a combination of factors available to surgeons before the end of an emergent laparotomy, the probability of OS-SSI could be accurately estimated using this retrospective cohort. A Web-based calculator is under design to allow the real-time estimation of probability of OS-SSI intraoperatively. Prospective validation of its generalizability to other trauma cohorts and of its utility at the point of care is required. LEVEL OF EVIDENCE:Prognostic study, level IV.

Project description:BackgroundOrgan/space surgical site infection (organ/space SSI) is a serious postoperative complication, closely related to a poor prognosis. Few studies have attempted to stratify the risk of organ/space SSI for patients with advanced digestive system cancer. This study aimed to identify a simple risk stratification for these patients based on perioperative factors.MethodsThe study was based on two randomized controlled trials (RCT) (NCT02715076, ChiCTR-IPR-17011099), including 839 patients undergoing elective radical resection of advanced digestive system cancer. The primary outcome was organ/space SSI within 30 days after surgery. Multivariable logistic regression model was used to identify risk factors. The risk of organ/space SSI stratified over those risk factors was compared using chi-square tests and the relative risk (RR) was estimated.ResultsAmong the 839 patients, 51 developed organ/space SSI (6.1%) within 30 days after surgery. According to the multivariable logistic regression model, 3 procedure types, including gastrectomy (OR=8.22, 95% CI: 2.71-24.87, P<0.001), colorectal resection (OR=8.65, 95% CI: 3.13-23.85, P<0.001) and pancreatoduodenectomy (OR=7.72, 95% CI: 2.95-20.21, P<0.001), as well as anaesthesia time > 4 h (OR=2.38, 95% CI: 1.08-5.27, P=0.032) and prolonged ICU stay (OR=4.10, 95% CI: 1.67-10.10, P=0.002), were risk factors for postoperative organ/space SSI. The number of risk factors was significantly associated with an increased risk of organ/space SSI (P<0.001), which was 2.8% in patients with 0-1 risk factor (RR=0.20, 95% CI: 0.11-0.35), 13.0% in patients with 2 risk factors (RR=3.64, 95% CI: 2.14-6.20) and 35.7% in patients with 3 risk factors (RR=6.41, 95% CI: 3.01-13.65).ConclusionThis study is a preliminary exploratory and provides a simple risk stratification to identify the risk of postoperative organ/space SSI for patients with advanced digestive system cancer. Further research is needed to validate and generalize the results in a wider population.Clinical trial registrationClinicalTrials.gov, identifier NCT02715076; Chinese Clinical Trial Registry [https://www.chictr.org.cn/enindex.aspx], identifier ChiCTR-IPR-17011099.

Project description:BackgroundThe growing spread of antimicrobial resistance (AMR) is accepted as a threat to humans, animals and the environment. This threat is considered to be both country specific and global, with bacteria resistant to antibiotic treatment geographically dispersed. Despite this, we have very few Australian estimates available that use national surveillance data supplemented with measures of risk, to generate reliable and actionable measures of AMR impact. These data are essential to direct policies and programs and support equitable healthcare resource utilisation. Importantly, such data can lead to implementation of programs to improved morbidity and mortality of patients with a resistant infection.MethodsUsing data from a previous case-cohort study, we estimated the AMR-associated health and economic impact caused by five hospital-associated AMR pathogens (Enterococcus spp., E. coli, K. pneumoniae, P. aeruginosa and S. aureus) in patients with a bloodstream, urinary tract, or respiratory tract infection in Australia in 2020. We estimated disease burden based on the counterfactual scenario in which all AMR infections were replaced by no infection.We used a population-level simulation model to compute AMR-associated mortality, loss of quality-adjusted life years and costs.FindingsIn 2020, there were 1,031 AMR-associated deaths (95% uncertainty interval [UI] 294, 2,615) from the five resistant hospital-associated infections in Australia. The greatest odds of dying were from respiratory infections (ceftazidime-resistant P. aeruginosa) and bloodstream infections, both resulting in high hospital and premature death costs. MRSA bacteraemia contributed the most to hospital costs (measured as bed-days) as patients with this infection resulted in additional 12,818 (95% UI 7246, 19966) hospital bed-days and cost the hospitals an extra $24,366,741 (95%UI $13,774,548, $37,954,686) per year. However, the cost of premature death from five resistant pathogens was $438,543,052, which was by far greater than the total hospital cost ($71,988,858). We estimate a loss of 27,705 quality-adjusted life years due to the five AMR pathogens.InterpretationThese are the first Australian estimates of AMR-associated health and economic impact. Country-level estimates of AMR impact are needed to provide local evidence to better inform programs and health policies to reduce morbidity and mortality associated with infection. The burden in hospital is likely an underestimate of the impact of AMR due to community-associated infections where data are limited, and the AMR burden is high. This should now be the focus of future study in this area.FundingTMW was supported by the Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE) (grant number GNT1116530) Fellowship.

Project description:ObjectivesDetermine the impact of infections on direct costs and healthcare resource use in England for patients undergoing intramedullary nailing (IMN) for tibial shaft fractures.DesignNon-concurrent cohort based on retrospectively collected data with 2-year follow-up.SettingEngland.ParticipantsThe study population included adult patients (≥18 years) in England with a diagnosis of tibial shaft fracture (International Classification of Diseases-10, S822) in the inpatient setting between May 2003 and June 2017 followed by a procedure for IMN for tibial shaft fracture within 30 days. Patient data were derived from the Clinical Practice Research Datalink linked to National Health Service Hospital Episode Statistics datasets.Primary independent variableInfection.Primary and secondary outcome measuresThe primary outcome was total inpatient costs from index stay admission through 1 year of follow-up. Secondary outcome included cumulative total healthcare costs, and resource utilisation at 30 days, 90 days, 1 year and 2 years.ResultsOverall, 805 patients met the inclusion criteria. At index inpatient stay, 3.7% had a post-IMN infection, rising to 11.7% at 1 year. One-year inpatient costs were 80% higher for patients with infection (p<0.001). Total costs were estimated to be £14 756 (95% CI £13 123 to £16 593) for patients with infection versus £8279 (95% CI £7946 to £8626). Length of stay (LOS), readmission and reoperation were the key drivers of healthcare costs (all p<0.001). After adjustment, LOS was higher by 109% (95% CI 62% to 169%), from 10.5 days to 21.9 days, for patients with infection. The odds of being readmitted or requiring reoperation were higher by 5.18 times (95% CI 3.01 to 9.13) and 2.47 times (95% CI 1.48 to 4.09), respectively, for patients with infection versus those without infection.ConclusionsPost-IMN infection significantly increases inpatient costs, LOS, readmissions and reoperations associated with tibial fracture fixation. Healthcare burden could be reduced through novel surgical site infection prevention strategies.

Project description:BackgroundHigh-quality outcomes data is crucial for continued surgical quality improvement. Outcomes are generally captured through structured administrative data or through manual curation of unstructured electronic health record (EHR) data. The aim of this study was to apply natural language processing (NLP) to chart notes in the EHR to accurately capture postoperative superficial surgical site infections (SSSIs).MethodsDeep Learning (DL) NLP models were trained on data from 389,865 surgical cases across all 11 hospitals in the Capital Region of Denmark. Surgical cases in the training dataset were performed between January 01st, 2017, and October 30th, 2021. We trained a forward reading and a backward reading universal language model on unlabeled postoperative chart notes recorded within 30 days of a surgical procedure. The two language models were subsequently finetuned on labeled data for the classification of SSSIs. Validation and testing were performed on surgical cases performed during the month of November 2021. We propose two different use cases: a stand-alone machine learning (SAM) pipeline and a human-in-the-loop (HITL) pipeline. Performances of both pipelines were compared to administrative data and to manual curation.ResultsThe models were trained on 3,983,864 unlabeled chart notes and finetuned on 1,231,656 labeled notes. Models had a test area under the receiver operating characteristic curves (ROC AUC) of 0.989 on individual chart notes and 0.980 on an aggregated case level. The SAM pipeline had a sensitivity of 0.604, a specificity of 0.996, a positive predictive value (PPV) of 0.763, and a negative predictive value (NPV) of 0.991. Prior to human review, the HITL pipeline had a sensitivity of 0.854, a specificity of 0.987, a PPV of 0.603, and a NPV of 0.997.ConclusionThe performance of the SAM pipeline was superior to administrative data, and significantly outperformed previously published results. The performance of the HITL pipeline approached that of manual curation.

Dataset Information

Unique predictors and economic burden of superficial and deep/organ space surgical site infections following pancreatectomy.

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Unique predictors and economic burden of superficial and deep/organ space surgical site infections following pancreatectomy.

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