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Is it time to measure complications from the National Trauma Data Bank? A longitudinal analysis of recent reporting trends.

ABSTRACT: BACKGROUND:Payers have approached select complications as never events, yet there is rationale that achieving a zero incidence of these events is impractical. Prior 2005 National Trauma Data Bank (NTDB) analysis showed high rates (37%) of centers reporting no complications data making national estimates for determining standardized complication rates difficult to ascertain. METHODS:The 2008-2012 NTDB National Sample Program nationally weighted files were used to calculate yearly national estimates. Rates were compared in all centers and those reporting complications data. Hospital characteristics were compared using Student t test. In 2011, an other complication category was introduced; overall rates were calculated with and without this category. Yearly estimates were reported for patients receiving care within centers reporting complications data. RESULTS:From 2008-2012 NTDB, there were raw data on 3,657,884 patients. A total of 594,894 patients (16.3%) experienced one or more complications (82.7% one complication; 17.3% two or more complications). Excluding the other complication category, the overall weighted rate was 8.4% to 9.2%. Pneumonia was the most common complication (2.7-3.0%), occurring at twice the 2005 rate. The number of centers reporting no complications data dropped to 8.1% in 2011 (2008, 14.5%; 2009, 18.2%; 2010, 15.9%; 2012, 8.9%). By 2012, nearly all level I centers reported complications, whereas 46.4% of level IVs reported none (I 0.5%, II 2.7%, III 8.5%, p = 0.04). Data were reported the least frequently in nonteaching hospitals (15.8%, p = 0.007), those in the South (19.6%, p = 0.007), and those with less than 200 beds (23.6%, p = 0.005). CONCLUSION:Overall rates of complications from 2008 to 2012 were nearly twofold higher than 2005 data. Reporting has increased, and NTDB may provide a valuable platform for establishing rational and achievable measures for specific complications. LEVEL OF EVIDENCE:Prognostic and epidemiological, level IV.

SUBMITTER: Robles AJ

PROVIDER: S-EPMC7269193 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Is it time to measure complications from the National Trauma Data Bank? A longitudinal analysis of recent reporting trends.

Robles Anamaria J AJ Conroy Amanda S AS Cohen Mitchell J MJ Callcut Rachael A RA

The journal of trauma and acute care surgery 20190201 2

<h4>Background</h4>Payers have approached select complications as never events, yet there is rationale that achieving a zero incidence of these events is impractical. Prior 2005 National Trauma Data Bank (NTDB) analysis showed high rates (37%) of centers reporting no complications data making national estimates for determining standardized complication rates difficult to ascertain.<h4>Methods</h4>The 2008-2012 NTDB National Sample Program nationally weighted files were used to calculate yearly n ...[more]

PMID: 30489507

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Project description:Background/Objective: Traumatic intracranial hemorrhage (tICH) accounts for significant trauma morbidity and mortality. Several studies have developed prognostic models for tICH outcomes, but previous models face limitations, including poor generalizability and limited accuracy. The objective was to develop a prognostic model and determine predictors of mortality using the largest trauma database in the U.S., applying rigorous analytical methodology with true hold-out-set model validation. Methods: We identified 248,536 patients in the National Trauma Data Bank (NTDB) from 2012 to 2016 with a diagnosis code associated with tICH. For each admission, we collected demographic information, systolic blood pressure, blood alcohol level (BAL), Glasgow Coma Score (GCS), Injury Severity Score (ISS), presence of epidural/subdural/subarachnoid/intraparenchymal hemorrhage, comorbidities, complications, trauma center level, and trauma center region. Our final study population was 212,666 patients following exclusion of records with missing data. The dependent variable was patient death. Linear support vector machine (SVM) classification was carried out with recursive feature selection. Model performance was assessed using holdout 10-fold cross-validation. Results: Cross-validation demonstrated a mean accuracy of 0.792 (95% CI 0.783-0.799). Accuracy, precision, recall, and AUC were 0.827, 0.309, 0.750, and 0.791, respectively. In the final model, high ISS, advanced age, subdural hemorrhage, and subarachnoid hemorrhage were associated with increased mortality, while high GCS verbal and motor subscores, current smoker, BAL beyond the legal limit, and level 1 trauma center were associated with decreased mortality. Conclusions: A linear SVM model was developed for tICH, with nine features selected as predictors of mortality. These findings are applicable to multiple hemorrhage subtypes and may benefit the triage of high risk patients upon admission. While many studies have attempted to create models to predict mortality in TBI, we sought to confirm those predictors using modern modeling approaches, machine learning, and true hold-out test sets, using the largest available TBI database in the U.S. We find that while the predictors we identify are consistent with prior reports, overall prediction accuracy is somewhat lower than prior reports when assessed more rigorously.

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Is it time to measure complications from the National Trauma Data Bank? A longitudinal analysis of recent reporting trends.

Publications

Is it time to measure complications from the National Trauma Data Bank? A longitudinal analysis of recent reporting trends.

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