Changes in the accuracy of administrative data for the detection of surgical site infections.
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ABSTRACT:
SUBMITTER: Bucher BT
PROVIDER: S-EPMC9040539 | biostudies-literature | 2021 Sep
REPOSITORIES: biostudies-literature
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Changes in the accuracy of administrative data for the detection of surgical site infections.
Bucher Brian T BT Yang Meng M Arndorfer Julie J Frame Cherie C Orton Jan J Samore Matthew H MH Dascomb Kristin K KK
Infection control and hospital epidemiology 20201217 9
We performed a retrospective analysis of the changes in accuracy of International Classification of Diseases, Clinical Modification (ICD-CM) diagnosis codes for colectomy and hysterectomy surgical site infection surveillance. After the transition from ICD-CM ninth edition to tenth edition codes, there was no significant change in the accuracy of these codes for SSI surveillance. ...[more]
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Project description:BackgroundSince primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms.MethodsAll patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets.ResultsOf 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90-0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402).ConclusionWe demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.
| S-EPMC8403439 | biostudies-literature
Project description:Introduction: The objective of the analysis was to determine the epidemiology of healthcare-associated infections (HAIs) in neurosurgical patients, paying special attention to two time points, 2003 and 2017, in order to evaluate the effectiveness of a surveillance program introduced in 2003 and efforts to reduce infection rates. Materials and methods: Continuous surveillance during 2003⁻2017 carried out using the HAI-Net methodology allowed us to detect 476 cases of HAIs among 10,332 patients staying in a 42-bed neurosurgery unit. The intervention in this before⁻after study (2003⁻2017) comprised standardized HAI surveillance with regular analysis and feedback. Results: The HAI incidence during the whole study was 4.6%. Surgical site infections (SSIs) accounted for 33% of all HAIs with an incidence rate of 1.5%. The remaining infections were pneumonia (1.1%) and bloodstream infections (0.9%). The highest SSI incidence concerned spinal fusion (FUSN, 2.2%), craniotomy (1.9%), and ventricular shunt (5.1%) while the associated total HAI incidence rates were 4.1%, 8.0%, and 18.6%, respectively. A significant reduction was found in HAI incidence between 2003 and 2017 in regard to the most common surgery types: laminectomy (4.5% vs. 0.8%); FUSN (11.8% vs. 0.8%); and craniotomy (10.1% vs. 0.4%). Significant changes were also achieved in selected elements of the unit's work: pre-hospitalization duration, hospital stay, and surgery length reductions. Simultaneously, the general condition of patients became significantly worse: there was an increase in patients' age and decreases in their general condition as expressed by ASA scores (The American Society of Anesthesiologists physical status classification system). Conclusions: HAI epidemiology changed substantially during the study period. Among the main types of HAI, SSIs were slightly predominant, but non-surgical HAIs accounted for almost two thirds of all infections; this indicates the need for surveillance of infection types other than SSIs in surgical patients. The implementation of active surveillance based on regular analysis and feedback led to a significant reduction in HAI incidence.
| S-EPMC6466004 | biostudies-literature
Project description:BackgroundCardiac implantable electronic device (CIED) surgical site infections (SSIs) have been outpacing the increases in implantation of these devices. While traditional surveillance of these SSIs by infection prevention and control would likely be the most accurate, this is not practical in many centers where resources are constrained. Therefore, we explored the validity of administrative data at identifying these SSIs.MethodsWe used a cohort of all patients with CIED implantation in Calgary, Alberta where traditional surveillance was done for infections from Jan 1, 2013 to December 31, 2019. We used this infection subgroup as our "gold standard" and then utilized various combinations of administrative data to determine which best optimized the sensitivity and specificity at identifying infection. We evaluated six approaches to identifying CIED infection using administrative data, which included four algorithms using International Classification of Diseases codes and/or Canadian Classification of Health Intervention codes, and two machine learning models. A secondary objective of our study was to assess if machine learning techniques with training of logistic regression models would outperform our pre-selected codes.ResultsWe determined that all of the pre-selected algorithms performed well at identifying CIED infections but the machine learning model was able to produce the optimal method of identification with an area under the receiver operating characteristic curve (AUC) of 96.8%. The best performing pre-selected algorithm yielded an AUC of 94.6%.ConclusionsOur findings suggest that administrative data can be used to effectively identify CIED infections. While machine learning performed the most optimally, in centers with limited analytic capabilities a simpler algorithm of pre-selected codes also has excellent yield. This can be valuable for centers without traditional surveillance to follow trends in SSIs over time and identify when rates of infection are increasing. This can lead to enhanced interventions for prevention of SSIs.
| S-EPMC9650806 | biostudies-literature
Project description:ObjectiveThe objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery.MethodsWe analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated performance with data from the subsequent 10 months (validation set). Models were developed both with and without data extracted from free text. We also evaluated the presence of an antibiotic prescription within 60 days after surgery as an independent indicator of SSI evidence. Our goal was to exceed 80% sensitivity and 10% positive predictive value (PPV).ResultsWe identified 234 surgeries with evidence of SSI among the 7910 surgeries available for analysis. We derived and validated an optimal prediction rule that included free text data using a random forest model (sensitivity = 0.9, PPV = 0.28). Presence of an antibiotic prescription had poor sensitivity (0.65) when applied to the derivation data but performed better when applied to the validation data (sensitivity = 0.84, PPV = 0.28).ConclusionsEHR data can facilitate SSI surveillance with adequate sensitivity and PPV.
| S-EPMC7646895 | biostudies-literature
Project description:ObjectiveThe objective of this study was to identify risk factors for surgical site infection from digestive, thoracic and orthopaedic system surgeries using clinical and data-driven cut-off values. A second objective was to compare the identified risk factors in this study to risk factors identified in literature.Summary background dataRetrospective data of 3 250 surgical procedures performed in large tertiary care hospital in The Netherlands during January 2013 to June 2014 were used.MethodsPotential risk factors were identified using a literature scan and univariate analysis. A multivariate forward-step logistic regression model was used to identify risk factors. Standard medical cut-off values were compared with cut-offs determined from the data.ResultsFor digestive, orthopaedic and thoracic system surgical procedures, the risk factors identified were preoperative temperature of ≥38°C and antibiotics used at the time of surgery. C-reactive protein and the duration of the surgery were identified as a risk factors for digestive surgical procedures. Being an adult (age ≥18) was identified as a protective effect for thoracic surgical procedures. Data-driven cut-off values were identified for temperature, age and CRP which can explain the SSI outcome up to 19.5% better than generic cut-off values.ConclusionsThis study identified risk factors for digestive, orthopaedic and thoracic system surgical procedures and illustrated how data-driven cut-offs can add value in the process. Future studies should investigate if data-driven cut-offs can add value to explain the outcome being modelled and not solely rely on standard medical cut-off values to identify risk factors.
| S-EPMC7592760 | biostudies-literature
Project description:ContextMany of the publicly available health quality report cards are based on administrative data. ICD-9-CM codes in administrative data are not date stamped to distinguish between medical conditions present at the time of hospital admission and complications, which occur after hospital admission. Treating complications as preexisting conditions gives poor-performing hospitals "credit" for their complications and may cause some hospitals that are delivering low-quality care to be misclassified as average- or high-performing hospitals.ObjectiveTo determine whether hospital quality assessment based on administrative data is impacted by the inclusion of condition present at admission (CPAA) modifiers in administrative data as a date stamp indicator.Design, setting, and patientsRetrospective cohort study based on 648,866 inpatient admissions between 1998 and 2000 for coronary artery bypass graft (CABG) surgery, coronary angioplasty (PTCA), carotid endarterectomy (CEA), abdominal aortic aneurysm (AAA) repair, total hip replacement (THR), acute MI (AMI), and stroke using the California State Inpatient Database which includes CPAA modifiers. Hierarchical logistic regression was used to create separate condition-specific risk adjustment models. For each study population, one model was constructed using only secondary diagnoses present at admission based on the CPAA modifier: "date stamp" model. The second model was constructed using all secondary diagnoses, ignoring the information present in the CPAA modifier: the "no date stamp model." Hospital quality was assessed separately using the "date stamp" and the "no date stamp" risk-adjustment models.ResultsForty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 40 percent of the THR hospitals, and 33 percent of the AMI hospitals identified as low-performance hospitals by the "date stamp" models were not classified as low-performance hospitals by the "no date stamp" models. Fifty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 50 percent of the CEA hospitals, and 36 percent of the AMI hospitals identified as low-performance hospitals by the "no date stamp" models were not identified as low-performance hospitals by the "date stamp" models. The inclusion of the CPAA modifier had a minor impact on hospital quality assessment for AAA repair, stroke, and CEA.ConclusionThis study supports the hypothesis that the use of routine administrative data without date stamp information to construct hospital quality report cards may result in the mis-identification of hospital quality outliers. However, the CPAA modifier will need to be further validated before date stamped administrative data can be used as the basis for health quality report cards.
| S-EPMC1797077 | biostudies-literature
Project description:BackgroundHealthcare-associated infections impose a significant burden on the healthcare system. Current methods for detecting these infections are constrained by combinations of high cost, long processing times and imperfect accuracy, reducing their effectiveness.MethodsThis study examined whether the amount of time a patient spends on a ward with other patients clinically suspected of infection, termed 'co-presence', can be used as a tool to predict subsequent healthcare-associated infection. Compared with contact tracing, this leverages passively collected electronic data rather than manually collected data, allowing for improved monitoring. All 133,304 inpatient records between 2011 and 2015 were abstracted from a healthcare system in the UK. The area under the receiver-operator curve (AUROC) for each of five pathogens was calculated based on co-presence time, sensitivity and specificity of the test, and how much earlier co-presence would have predicted infection for the true-positive cases.FindingsFor the five pathogens, AUROC ranged from 0.92 to 0.99, and was 0.52 for the negative control. Optimal cut-points of co-presence ranged from 25 to 59 h, and would have led to detection of true-positive cases up to an average of 1 day earlier.InterpretationThese findings show that co-presence time would help to predict healthcare-acquired infection, and would do so earlier than the current standard of care. Using this measure prospectively in hospitals based on real-time data could limit the consequences of infection, both by being able to treat individual infected patients earlier, and by preventing potential secondary infections stemming from the original infected patient.
| S-EPMC7395302 | biostudies-literature
Project description:The Sars-CoV-2 pandemic catalysed integration of telemedicine worldwide. This systematic review assesses it's accuracy for diagnosis of Surgical Site Infection (SSI). Databases were searched for telemedicine and wound infection studies. All types of studies were included, only paired designs were taken to meta-analysis. QUADAS-2 assessed methodological quality. 1400 titles and abstracts were screened, 61 full text reports were assessed for eligibility and 17 studies were included in meta-analysis, mean age was 47.1 ± 13.3 years. Summary sensitivity and specificity was 87.8% (95% CI, 68.4-96.1) and 96.8% (95% CI 93.5-98.4) respectively. The overall SSI rate was 5.6%. Photograph methods had lower sensitivity and specificity at 63.9% (95% CI 30.4-87.8) and 92.6% (95% CI, 89.9-94.5). Telemedicine is highly specific for SSI diagnosis is highly specific, giving rise to great potential for utilisation excluding SSI. Further work is needed to investigate feasibility telemedicine in the elderly population group.
| S-EPMC9349203 | biostudies-literature
Project description:Since the COVID-19 pandemic there has been a rapid uptake and utilisation of telemedicine in all aspects of healthcare. This presents a key opportunity in surgical site infection surveillance. Remote follow up methods have been used via telephone, with photographs and questionnaires for post-operative reviews with varying results. This review therefore aims to comprehensively synthesise available evidence for the diagnostic accuracy of all forms of SSI telemedicine monitoring. The protocol has been established as per both PRISMA-P (S1 Table) and the Cochrane handbook for reviews of diagnostic test accuracy. Medline, Embase, CENTRAL and CINAHL will be searched using a complete search strategy developed with librarian input, in addition to google scholar and hand searching. All study designs with patients over 18 and undergone a primarily closed surgical procedure will be eligible. Index tests will include all forms of telemedicine and a subgroup analysis performed for each of these. Comparative tests must include face to face review, and all reference standards will be included again for sub-group analyses. Search results will be screened by two investigators independently with a third providing consensus review on disagreements. Methodological quality will be assessed using the QUADAS-2 tool, first validated by two investigators as per the Cochrane handbook. Exploratory analysis will formulate summary receiver operating characteristic curves and forest plots with estimates of sensitivity and specificity of the included studies. Sources of heterogeneity will be identifying and investigated through further analysis. Potential benefits of telemedicine integration in surgical practice will reduce cost and travel time to patients in addition to avoiding wasted clinic appointments, important considerations in a peri-pandemic era. To avoid missed or further complications, there must be confidence in the ability to diagnose infection. This review will systematically determine whether telemedicine is accurate for surgical site infection diagnosis, which methods are well established and if further research is indicated.
| S-EPMC9671442 | biostudies-literature
Project description:ObjectiveTo test the accuracy of reporting present-on-admission (POA) and to assess whether POA reporting accuracy differs by hospital characteristics.Data sourcesWe performed an audit of POA reporting of secondary diagnoses in 1,059 medical records from 48 California hospitals.Study designWe used patient discharge data (PDD) to select records with secondary diagnoses that are powerful predictors of mortality and could potentially represent comorbidities or complications among patients who either had a primary procedure of a percutaneous transluminal coronary angioplasty or a primary diagnosis of acute myocardial infarction, community-acquired pneumonia, or congestive heart failure. We modeled the relationship between secondary diagnoses POA reporting accuracy (over-reporting and under-reporting) and hospital characteristics.Data collectionWe created a gold standard from blind reabstraction of the medical records and compared the accuracy of the PDD against the gold standard.Principal findingsThe PDD and gold standard agreed on POA reporting in 74.3 percent of records, with 13.7 percent over-reporting and 11.9 percent under-reporting. For-profit hospitals tended to overcode secondary diagnoses as present on admission (odds ratios [OR] 1.96; 95 percent confidence interval [CI] 1.11, 3.44), whereas teaching hospitals tended to undercode secondary diagnoses as present on admission (OR 2.61; 95 percent CI 1.36, 5.03).ConclusionsPOA reporting of secondary diagnoses is moderately accurate but varies by hospitals. Steps should be taken to improve POA reporting accuracy before using POA in hospital assessments tied to payments.
| S-EPMC3393034 | biostudies-literature