Search

BackgroundVenous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are the most commonly used risk-assessment models to quantify VTE risk. Both models perform well in select, high-risk cohorts. While VTE risk-stratification is recommended for all hospital admissions, few studies have evaluated the models in a large, unselected cohort of patients.MethodsWe analyzed consecutive first hospital admissions of 1,252,460 unique surgical and non-surgical patients to 1,298 VA facilities nationwide between January 2016 and December 2021. Caprini and Padua scores were generated using the VA’s national data repository. We first assessed the ability of the two RAMs to predict VTE within 90 days of admission. In secondary analyses, we evaluated prediction at 30 and 60 days, in surgical versus non-surgical patients, after excluding patients with upper extremity DVT, in patients hospitalized ≥72 hours, after including all-cause mortality in the composite outcome, and after accounting for prophylaxis in the predictive model. We used area under the receiver-operating characteristic curves (AUC) as the metric of prediction.ResultsA total of 330,388 (26.4%) surgical and 922,072 (73.6%) non-surgical consecutively hospitalized patients (total n=1,252,460) were analyzed. Caprini scores ranged from 0-28 (median, interquartile range: 4, 3-6); Padua scores ranged from 0-13 (1, 1-3). The RAMs showed good calibration and higher scores were associated with higher VTE rates. VTE developed in 35,557 patients (2.8%) within 90 days of admission. The ability of both models to predict 90-day VTE was low (AUCs: Caprini 0.56 [95% CI 0.56-0.56], Padua 0.59 [0.58-0.59]). Prediction remained low for surgical (Caprini 0.54 [0.53-0.54], Padua 0.56 [0.56-0.57]) and non-surgical patients (Caprini 0.59 [0.58-0.59], Padua 0.59 [0.59-0.60]). There was no clinically meaningful change in predictive performance in patients admitted for ≥72 hours, after excluding upper extremity DVT from the outcome, after including all-cause mortality in the outcome, or after accounting for ongoing VTE prophylaxis.ConclusionsCaprini and Padua risk-assessment model scores have low ability to predict VTE events in a cohort of unselected consecutive hospitalizations. Improved VTE risk-assessment models must be developed before they can be applied to a general hospital population.

The effects of stroke and delirium on postdischarge cognition and patient-centered health outcomes after surgical aortic valve replacement (SAVR) are not well characterized. Here, we assess the impact of postoperative stroke and delirium on these health outcomes in SAVR patients at 90 days.Patients (N = 383) undergoing SAVR (41% received concomitant coronary artery bypass graft) enrolled in a randomized trial of embolic protection devices underwent serial neurologic and delirium evaluations at postoperative days 1, 3, and 7 and magnetic resonance imaging at day 7. Outcomes included 90-day functional status, neurocognitive decline from presurgical baseline, and quality of life.By postoperative day 7, 25 (6.6%) patients experienced clinical stroke and 103 (28.5%) manifested delirium. During index hospitalization, time to discharge was longer in patients experiencing stroke (hazard ratio, 0.62; 95% confidence interval [CI], 0.42-0.94; P = .02) and patients experiencing delirium (hazard ratio, 0.68; 95% CI, 0.54-0.86; P = .001). At day 90, patients experiencing stroke were more likely to have a modified Rankin score2 (odds ratio [OR], 5.9; 95% CI, 1.7-20.1; P = .01), depression (OR, 5.3; 95% CI, 1.6-17.3; P = .006), a lower 12-Item Short Form Survey physical health score (adjusted mean difference -3.3 ± 1.9; P = .08), and neurocognitive decline (OR, 7.8; 95% CI, 2.3-26.4; P = .001). Delirium was associated with depression (OR, 2.2; 95% CI, 0.9-5.3; P = .08), lower 12-Item Short Form Survey physical health (adjusted mean difference -2.3 ± 1.1; P = .03), and neurocognitive decline (OR, 2.2; 95% CI, 1.2-4.0; P = .01).Stroke and delirium occur more frequently after SAVR than is commonly recognized, and these events are associated with disability, depression, cognitive decline, and poorer quality of life at 90 days postoperatively. These findings support the need for new interventions to reduce these events and improve patient-centered outcomes.

Background: Medicare administrative data has been proposed as an efficient alternative for long-term event detection. The accuracy of these data relative to investigator reported and event adjudicated results are unknown. Methods: We linked 3217 older patients (65+ years) with acute coronary syndromes (ACS) from 4 ACS clinical trials to Medicare inpatient standard analytic files (SAFs) using a deterministic linkage and indirect patient identifiers (e.g., sex, date of birth, date of index hospitalization). We compared event ascertainment for death, all-cause rehospitalization, myocardial infarction (MI), heart failure and revascularization to 180 days after hospital discharge. Up to four Medicare ICD-9-CM coding algorithms were evaluated for each outcome. Sensitivity, specificity and positive and negative predictive values were assessed for Medicare events considering trial records as the ‘gold standard’. Results: The 180 day incidences of adverse events following discharge were: death 4.6%, all-cause rehospitalization 50.9%, MI 5.6%, heart failure 15.1%, and any revascularization 13.2%. For most outcomes, Medicare claims displayed similar or higher event rates as those reported for trials. In general, Medicare claims had high sensitivity, specificity, and positive and negative predictive value when compared to trial outcomes; however, these differed depending on the Medicare coding algorithm used (Table). Conclusions: Medicare claims detect similar or greater numbers of events as those identified by existing clinical trial mechanisms. These data demonstrate the potential utility of administrative claims for efficient long-term follow-up of older patients enrolled in clinical trials or post-market surveillance efforts. Further investigation is needed to both optimize Medicare coding algorithms and determine which mechanism is most accurate among discordant reported events.