Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates

Utilization of a Dual Surveillance Program to Reduce Surgical-site Infections

Background:

Surveillance plays a pivotal role in the surgical-site infections (SSIs) prevention through identifying infections, monitoring changes in infection rates, and evaluating the effectiveness of intervention strategies.

Methods:

This retrospective study reviewed SSI surveillance systems implemented at the Children’s National Health System in 3 phases between 2007 and 2016 including all surgical procedures. The targeted surveillance was conducted in cardiovascular, spinal fusion, and ventricular shunt surgeries and required an infection preventionist (IP) to review all procedures, to identify SSIs that meet the CDC’s National Healthcare Safety Network definition. SSIs in the remaining surgical procedures were identified through the review of positive microbiology reports daily and followed by full chart review if the specimen type and/or patient location were suggestive of a surgical history. Timely feedback of SSI to stakeholders was the primary mode of intervention, with additional interventions implemented for the 3 targeted surveillance procedures. Data collected between 2013 and 2016 were analyzed to account for a definition change in 2013.

Results:

For the 3 targeted surveillance procedures, IP reviewed 2,255 procedures and identified 43 SSIs. For the remaining procedures, IPs identified 123 SSIs confirmed by one or more pathogens. The overall SSI rate had a 31% decrease. The cardiovascular and spinal fusion SSI rate had a 61% and 84% decrease, respectively. The ventricular shunt SSI rate increased 29% due to 2 episodes of recurrent infections in 2 patients.

Conclusions:

It is prudent for hospitals to continue monitoring SSI by establishing surveillance programs with optimal approaches.

Use of Medicare Claims to Rank Hospitals by Surgical Site Infection Risk following Coronary Artery Bypass Graft Surgery

Objective.

To evaluate whether longitudinal insurer claims data allow reliable identification of elevated hospital surgical site infection (SSI) rates.

Design.

We conducted a retrospective cohort study of Medicare beneficiaries who underwent coronary artery bypass grafting (CABG) in US hospitals performing at least 80 procedures in 2005. Hospitals were assigned to deciles by using case mix–adjusted probabilities of having an SSI-related inpatient or outpatient claim code within 60 days of surgery. We then reviewed medical records of randomly selected patients to assess whether chart-confirmed SSI risk was higher in hospitals in the worst deciles compared with the best deciles.

Participants.

Fee-for-service Medicare beneficiaries who underwent CABG in these hospitals in 2005.

Results.

We evaluated 114,673 patients who underwent CABG in 671 hospitals. In the best decile, 7.8% (958/12,307) of patients had an SSI-related code, compared with 24.8% (2,747/11,068) in the worst decile (P<.001). Medical record review confirmed SSI in 40% (388/980) of those with SSI-related codes. In the best decile, the chart-confirmed annual SSI rate was 3.2%, compared with 9.4% in the worst decile, with an adjusted odds ratio of SSI of 2.7 (confidence interval, 2.2–3.3; P<.001) for CABG performed in a worst-decile hospital compared with a best-decile hospital.

Conclusions.

Claims data can identify groups of hospitals with unusually high or low post-CABG SSI rates. Assessment of claims is more reproducible and efficient than current surveillance methods. This example of secondary use of routinely recorded electronic health information to assess quality of care can identify hospitals that may benefit from prevention programs.

Impact of Medicare's payment policy on mediastinitis following coronary artery bypass graft surgery in US hospitals

BACKGROUND

The Centers for Medicare and Medicaid Services (CMS) implemented a policy in October 2008 to eliminate additional Medicare payment for mediastinitis following coronary artery bypass graft (CABG) surgery.

OBJECTIVE

To evaluate the impact of this policy on mediastinitis rates, using Medicare claims and National Healthcare Safety Network (NHSN) prospective surveillance data.

METHODS

We used an interrupted time series design to compare mediastinitis rates before and after the policy, adjusted for secular trends. Billing rates came from Medicare inpatient claims following 638,761 CABG procedures in 1,234 US hospitals (January 2006-September 2010). Prospective surveillance rates came from 151 NHSN hospitals in 29 states performing 94,739 CABG procedures (January 2007-September 2010). Logistic regression mixed-effects models estimated trends for mediastinitis rates.

RESULTS

We found a sudden drop in coding for index admission mediastinitis at the time of policy implementation (odds ratio, 0.36 [95% confidence interval (CI), 0.23-0.57]) and a decreasing trend in coding for index admission mediastinitis in the postintervention period compared with the preintervention period (ratio of slopes, 0.83 [95% CI, 0.74-0.95]). However, we saw no impact of the policy on infection rates as measured using NHSN data. Our results were not affected by changes in patient risk over time, heterogeneity in hospital demographics, or timing of hospital participation in NHSN.

CONCLUSIONS

The CMS policy of withholding additional Medicare payment for mediastinitis on the basis of claims-based evidence of infection was associated with changes in coding for infections but not with changes in actual infection rates during the first 2 years after policy implementation.

The dilemma of assessment bias in infection control research

Infection control studies often rely on infection endpoints to determine whether interventions are effective. However, many infection outcomes, including those defined by standardized surveillance criteria, involve some subjective judgment for determination. Studies that use unblinded ascertainment of subjective infection endpoints are at risk of assessment bias. Unfortunately, infection control studies have not routinely accounted for assessment bias. To ensure validity, infection control studies should incorporate study design elements to control assessment bias, such as blinded assessment or use of objective outcome measures.

Automated detection of external ventricular and lumbar drain-related meningitis using laboratory and microbiology results and medication data

Objective

Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse.

Methods

As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability.

Results

537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates.

Conclusion

A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections.