Dataset Information

Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.

ABSTRACT:

Objective

Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.

Data sources

Veterans Health Administration's Corporate Data Warehouse.

Study design

Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE.

Principal findings

Of 1,730 FY2012-2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c-statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4-0.5 predicted probability of an AE, PPV was 38 percent.

Conclusions

The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE.

SUBMITTER: Mull HJ

PROVIDER: S-EPMC6232409 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.

Mull Hillary J HJ Itani Kamal M F KMF Pizer Steven D SD Charns Martin P MP Rivard Peter E PE McIntosh Nathalie N Hawn Mary T MT Rosen Amy K AK

Health services research 20180827 6

<h4>Objective</h4>Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.<h4>Data sources</h4>Veterans Health Administration's Corporate Data Warehouse.<h4>Study design</h4>Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review ...[more]

PMID: 30151826

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Project description:ImportanceThe Veterans Health Administration (VHA) reports multiple indicators of hospital surgical performance, including hospital risk-standardized 30-day readmission rates (RSRRs). Currently, most routinely reported measures do not include readmissions that occur outside VHA hospitals. The impact of readmissions outside the VHA on hospital RSRR is not known.ObjectiveTo measure the impact of including non-VHA readmissions on VHA hospital performance rankings for 30-day readmission.Design, setting, and participantsThis retrospective cohort study included patients aged at least 65 years from 2013 to 2019 from the Veterans Affairs Surgical Quality Improvement Program linked to patient-level data from the VHA and Medicare. Data were limited to patients with VHA and Medicare enrollment during the year prior to surgery. Data were analyzed from November 2023 through July 2024.Main outcomes and measuresThe main outcome was readmissions to acute care VHA or non-VHA hospitals within 30 days of discharge. VHA hospital-level RSRRs were estimated using separate generalized linear mixed-effects risk adjustment models that alternatively included VHA-only or VHA plus non-VHA readmissions. VHA hospitals were then stratified into quintiles based on RSRRs derived using VHA-only or VHA plus non-VHA readmissions. Changes in hospital performance quintiles with the addition of non-VHA readmissions were calculated, and characteristics of VHA hospitals most impacted by including non-VHA readmissions were evaluated.ResultsThe eligible cohort included 108 265 patients (mean [SD] age, 72.2 [6.5] years; 105 661 [97.6%] male) who underwent surgery in 104 VHA hospitals. The combined readmission rate was 14.0%. The proportion of readmissions occurring outside the VHA ranged from 0% to 55.3% across the 104 VHA hospitals (median, 20.9%). Using VHA and non-VHA readmissions, 24 VHA hospitals (23.1%) improved performance and 23 hospitals (22.1%) worsened performance, defined as a decrease or increase, respectively, of 1 or more RSRR quintiles. Improvements in hospital performance rank were associated with larger surgical volume (-7.48; 95% CI, -11.33 to 03.64; P < .001), urban location, greater surgical complexity (-9.86; 95% CI, -16.61 to -3.11; P = .005), and lower proportion of readmissions outside the VHA (-8.15; 95% CI, -12.75 to -3.55; P < .001).Conclusions and relevanceIn this cohort study, VHA hospitals whose readmission performance metric improved by including non-VHA readmissions had higher patient volume, higher complexity, and lower proportion of care outside the VHA. Thus, improving continuity of care may have a paradoxical effect of worsening VHA performance metrics.

Dataset Information

Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.

Objective

Data sources

Study design

Principal findings

Conclusions

Publications

Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.

Similar Datasets

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