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An Automated Surveillance Strategy to Identify Infectious Complications After Cardiac Implantable Electronic Device Procedures.

ABSTRACT: Background. ?The optimum approach for infectious complication surveillance for cardiac implantable electronic device (CIED) procedures is unclear. We created an automated surveillance tool for infectious complications after CIED procedures. Methods. ?Adults having CIED procedures between January 1, 2005 and December 31, 2011 at Duke University Hospital were identified retrospectively using International Classification of Diseases, 9th revision (ICD-9) procedure codes. Potential infections were identified with combinations of ICD-9 diagnosis codes and microbiology data for 365 days postprocedure. All microbiology-identified and a subset of ICD-9 code-identified possible cases, as well as a subset of procedures without microbiology or ICD-9 codes, were reviewed. Test performance characteristics for specific queries were calculated. Results. ?Overall, 6097 patients had 7137 procedures. Of these, 1686 procedures with potential infectious complications were identified: 174 by both ICD-9 code and microbiology, 14 only by microbiology, and 1498 only by ICD-9 criteria. We reviewed 558 potential cases, including all 188 microbiology-identified cases, 250 randomly selected ICD-9 cases, and 120 with neither. Overall, 65 unique infections were identified, including 5 of 250 reviewed cases identified only by ICD-9 codes. Queries that included microbiology data and ICD-9 code 996.61 had good overall test performance, with sensitivities of approximately 90% and specificities of approximately 80%. Queries with ICD-9 codes alone had poor specificity. Extrapolation of reviewed infectious rates to nonreviewed cases yields an estimated rate of infection of 1.3%. Conclusions. ?Electronic queries with combinations of ICD-9 codes and microbiologic data can be created and have good test performance characteristics for identifying likely infectious complications of CIED procedures.

SUBMITTER: Boggan JC

PROVIDER: S-EPMC4606845 | biostudies-other | 2015 Dec

REPOSITORIES: biostudies-other

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An Automated Surveillance Strategy to Identify Infectious Complications After Cardiac Implantable Electronic Device Procedures.

Boggan Joel C JC Baker Arthur W AW Lewis Sarah S SS Dicks Kristen V KV Durkin Michael J MJ Moehring Rebekah W RW Chen Luke F LF Knelson Lauren P LP Hegland Donald D DD Anderson Deverick J DJ

Open forum infectious diseases 20150901 4

Background. The optimum approach for infectious complication surveillance for cardiac implantable electronic device (CIED) procedures is unclear. We created an automated surveillance tool for infectious complications after CIED procedures. Methods. Adults having CIED procedures between January 1, 2005 and December 31, 2011 at Duke University Hospital were identified retrospectively using International Classification of Diseases, 9th revision (ICD-9) procedure codes. Potential infections were i ...[more]

PMID: 26484356

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Project description:ImportanceThe association of the nasal microbiome with outcomes in surgical patients is poorly understood.ObjectiveTo characterize the composition of nasal microbiota in patients undergoing clean elective surgical procedures and to examine the association between characteristics of preoperative nasal microbiota and occurrence of postoperative infection.Design, setting, and participantsUsing a nested matched case-control design, 53 individuals who developed postoperative infection were matched (approximately 3:1 by age, sex, and surgical procedure) with 144 individuals who were not infected (ie, the control group). The 2 groups were selected from a prospective cohort of patients undergoing surgical procedures at 2 tertiary care university hospitals in Baltimore, Maryland, who were at high risk for postoperative infectious complications. Included individuals were aged 40 years or older; had no history of autoimmune disease, immunocompromised state, immune-modulating medication, or active infection; and were scheduled to undergo elective cardiac, vascular, spinal, or intracranial surgical procedure. Data were analyzed from October 2015 through September 2020.ExposuresNasal microbiome cluster class served as the main exposure. An unsupervised clustering method (ie, grades of membership modeling) was used to classify nasal microbial samples into 2 groups based on features derived from 16S ribosomal RNA gene sequencing. The microbiome cluster groups were derived independently and agnostic of baseline clinical characteristics and infection status.Main outcomes and measuresComposite of surgical site infection, bacteremia, and pneumonia occurring within 6 months after surgical procedure.ResultsAmong 197 participants (mean [SD] age, 64.1 [10.6] years; 63 [37.7%] women), 553 bacterial taxa were identified from preoperative nasal swab samples. A 2-cluster model (with 167 patients in cluster 1 and 30 patients in cluster 2) accounted for the largest proportion of variance in microbial profiles using grades of membership modeling and was most parsimonious. After adjusting for potential confounders, the probability of assignment to cluster 2 was associated with 6-fold higher odds of infection after surgical procedure (odds ratio [OR], 6.18; 95% CI, 3.33-11.7; P < .001) independent of baseline clinical characteristics, including nasal carriage of Staphylococcus aureus. Intrasample (ie, α) diversity was inversely associated with infectious outcome in both clusters (OR, 0.57; 95% CI, 0.42-0.75; P < .001); however, probability of assignment to cluster 2 was associated with higher odds of infection independent of α diversity (OR, 4.61; 95% CI, 2.78-7.86; P < .001).Conclusions and relevanceThese findings suggest that the nasal microbiome was an independent risk factor associated with infectious outcomes among individuals who underwent elective surgical procedures and may serve as a biomarker associated with infection susceptibility in this population.

Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Dataset Information

An Automated Surveillance Strategy to Identify Infectious Complications After Cardiac Implantable Electronic Device Procedures.

Publications

An Automated Surveillance Strategy to Identify Infectious Complications After Cardiac Implantable Electronic Device Procedures.

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