Development and Validation of a Preoperative Surgical Site Infection Risk Score for Primary or Revision Knee and Hip Arthroplasty

2016 ◽  
Vol 98 (18) ◽  
pp. 1522-1532 ◽  
Author(s):  
Joshua S. Everhart ◽  
Rebecca R. Andridge ◽  
Thomas J. Scharschmidt ◽  
Joel L. Mayerson ◽  
Andrew H. Glassman ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Risk Score ◽  
Hip Arthroplasty ◽  
Infection Risk ◽  
Site Infection ◽  
Knee And Hip Arthroplasty ◽  
Development And Validation

scholarly journals A New Surgical Site Infection Risk Score: Infection Risk Index in Cardiac Surgery

2019 ◽  
Vol 8 (4) ◽  
pp. 480 ◽  
Author(s):  
Juan Bustamante-Munguira ◽  
Francisco Herrera-Gómez ◽  
Miguel Ruiz-Álvarez ◽  
Ana Hernández-Aceituno ◽  
Angels Figuerola-Tejerina
Keyword(s):  
Cardiac Surgery ◽  
Surgical Site Infection ◽  
Risk Score ◽  
Artery Bypass ◽  
Characteristic Curve ◽  
Risk Index ◽  
Bypass Graft ◽  
Test Sample ◽  
Infection Risk ◽  
Site Infection

Various scoring systems attempt to predict the risk of surgical site infection (SSI) after cardiac surgery, but their discrimination is limited. Our aim was to analyze all SSI risk factors in both coronary artery bypass graft (CABG) and valve replacement patients in order to create a new SSI risk score for such individuals. A priori prospective collected data on patients that underwent cardiac surgery (n = 2020) were analyzed following recommendations from the Reporting of studies Conducted using Observational Routinely collected health Data (RECORD) group. Study participants were divided into two periods: the training sample for defining the new tool (2010–2014, n = 1298), and the test sample for its validation (2015–2017, n = 722). In logistic regression, two preoperative variables were significantly associated with SSI (odds ratio (OR) and 95% confidence interval (CI)): diabetes, 3.3/2–5.7; and obesity, 4.5/2.2–9.3. The new score was constructed using a summation system for punctuation using integer numbers, that is, by assigning one point to the presence of either diabetes or obesity. The tool performed better in terms of assessing SSI risk in the test sample (area under the Receiver-Operating Characteristic curve (aROC) and 95% CI, 0.67/055–0.76) compared to the National Nosocomial Infections Surveillance (NNIS) risk index (0.61/0.50–0.71) and the Australian Clinical Risk Index (ACRI) (0.61/0.50–0.72). A new two-variable score to preoperative SSI risk stratification of cardiac surgery patients, named Infection Risk Index in Cardiac surgery (IRIC), which outperforms other classical scores, is now available to surgeons. Personalization of treatment for cardiac surgery patients is needed.

scholarly journals 1011Quick But Not Dirty: Short Operative Time and Surgical Site Infection Rates In Knee and Hip Arthroplasty Procedures

2014 ◽  
Vol 1 (suppl_1) ◽  
pp. S296-S296
Author(s):  
Kristen V. Dicks ◽  
Michael J. Durkin ◽  
Arthur W. Baker ◽  
Luke F. Chen ◽  
Deverick J. Anderson ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Hip Arthroplasty ◽  
Operative Time ◽  
Site Infection ◽  
Infection Rates ◽  
Short Operative Time ◽  
Knee And Hip Arthroplasty

Predicting cardiac surgical site infection: development and validation of the Barts Surgical Infection Risk tool

2020 ◽  
Vol 128 ◽  
pp. 57-65 ◽  
Author(s):  
Rosalie Magboo ◽  
Nicholas Drey ◽  
Jackie Cooper ◽  
Heather Byers ◽  
Alex Shipolini ◽  
...  
Keyword(s):  
Surgical Site Infection ◽  
Infection Risk ◽  
Site Infection ◽  
Surgical Infection ◽  
Development And Validation

scholarly journals Effect of multidimensional intervention to reduce surgical site infection rate after knee and hip arthroplasty

GERMS ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 10-22
Author(s):  
Samar Saeed Morsi ◽  
Abeer Omar ◽  
Gautam Hebbar ◽  
Mariam Al-Fadhli ◽  
Wafaa S Hamza
Keyword(s):  
Surgical Site Infection ◽  
Infection Rate ◽  
Hip Arthroplasty ◽  
Site Infection ◽  
Surgical Site Infection Rate ◽  
Knee And Hip Arthroplasty

Re-admissions After Diagnosis of Surgical Site Infection Following Knee and Hip Arthroplasty

2012 ◽  
Vol 40 (5) ◽  
pp. e189
Author(s):  
Keith Kaye ◽  
Odaliz E. Abreu-Lanfranco ◽  
Kyle Miletic ◽  
Emily Toth Martin ◽  
Tom Taylor
Keyword(s):  
Surgical Site Infection ◽  
Hip Arthroplasty ◽  
Site Infection ◽  
Knee And Hip Arthroplasty

scholarly journals Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries

2020 ◽  
Vol 41 (S1) ◽  
pp. s135-s136
Author(s):  
Flávio Souza ◽  
Braulio Couto ◽  
Felipe Leandro Andrade da Conceição ◽  
Gabriel Henrique Silvestre da Silva ◽  
Igor Gonçalves Dias ◽  
...  
Keyword(s):  
Data Collection ◽  
Infection Control ◽  
Surgical Site Infection ◽  
Hip Arthroplasty ◽  
Hospital Infection ◽  
Site Infection ◽  
Weight Decay ◽  
Hospital Infection Control ◽  
Belo Horizonte ◽  
Arthroplasty Surgery

Background: In 7 hospitals in Belo Horizonte, a city with >3,000,000 inhabitants, a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing arthroplasty surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, the MLPs (multilayer perceptron). Methods: Data were collected on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. All data used in the analysis during their routine SSI surveillance procedures were collected. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH automated hospital infection control system software to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing and 35% or 25% for validation). The results were compared by measuring AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: Of 1,246 records, 535 were intact for analysis. We obtained the following statistics: the average surgery time was 190 minutes (range, 145–217 minutes); the average age of the patients was 67 years (range, 9–103); the prosthetic implant index was 98.13%; the SSI rate was 1.49%, and the death rate was 1.21%. Regarding the prediction power, the maximum prediction power was 0.744. Conclusions: Despite the considerable loss rate of almost 60% of the database samples due to the presence of noise, it was possible to perform relevant sampling for the profile evaluation of hospitals in Belo Horizonte. For the predictive process, some configurations have results that reached 0.744, which indicates the usefulness of the structure for automated SSI monitoring for patients undergoing hip arthroplasty surgery. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com ), a mobile application was developed.Funding: NoneDisclosures: None

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