The aim of this study was to compare the rate of perioperative
complications following aseptic revision total hip arthroplasty
(THA) in patients aged ≥ 80 years with that in those aged <
80
years, and to identify risk factors for the incidence of serious
adverse events in those aged ≥ 80 years using a large validated
national database. Patients who underwent aseptic revision THA were identified in
the 2005 to 2015 National Surgical Quality Improvement Program (NSQIP)
database and stratified into two age groups: those aged <
80
years and those aged ≥ 80 years. Preoperative and procedural characteristics
were compared. Multivariate regression analysis was used to compare
the risk of postoperative complications and readmission. Risk factors
for the development of a serious adverse event in those aged ≥ 80
years were characterized.Aims
Patients and Methods
The aim of this study was to assess the influence of operating time on 30-day complications following total hip arthroplasty (THA). We identified patients aged 18 years and older who underwent THA between 2006 and 2016 from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database. We identified 131 361 patients, with a mean age of 65 years (Aims
Patients and Methods
The aim of this study was to investigate the differences in 30-day outcomes between patients undergoing revision for an infected total hip arthroplasty (THA) compared with an aseptic revision THA. This was a retrospective review of prospectively collected data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database, between 2012 and 2017, using Current Procedural Terminology (CPT) codes for patients undergoing a revision THA (27134, 27137, 27138). International Classification of Diseases Ninth Revision/Tenth Revision (ICD-9-CM, ICD-10-CM) diagnosis codes for infection of an implant or device were used to identify patients undergoing an infected revision THA. CPT-27132 coupled with ICD-9-CM/ICD-10-CM codes for infection were used to identify patients undergoing a two-stage revision. A total of 13 556 patients were included; 1606 (11.8%) underwent a revision THA due to infection and there were 11 951 (88.2%) aseptic revisions.Aims
Patients and Methods
Total hip arthroplasty (THA) is gaining popularity as a treatment for displaced femoral neck fractures (FNFs), especially in physiologically younger patients. While THA for osteoarthritis (OA) has demonstrated low complication rates and increased quality of life, results of THA for acute FNF are not as clear. Currently, a THA performed for FNF is included in an institutional arthroplasty bundle without adequate risk adjustment, potentially placing centres participating in fracture care at financial disadvantage. The purpose of this study is to report on perioperative complication rates after THA for FNF compared with elective THA performed for OA of the hip. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database between 2008 and 2016 was queried. Patients were identified using the THA Current Procedural Terminology (CPT) code and divided into groups by diagnosis: OA in one and FNF in another. Univariate statistics were performed. Continuous variables were compared between groups using Student’s Aims
Patients and Methods
Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity).Aims
Methods
