S0472 Epidemiology of Hospitalizations Due to Cyclical Vomiting Syndrome in the United States
Bibliographic record
Abstract
INTRODUCTION: Cyclic vomiting syndrome (CVS) is a functional gastrointestinal disorder leading to multiple hospitalizations and causes significant impairment to the quality of life. We aim to study the trends, characteristics and outcomes of CVS using a nationwide database. METHODS: We derived a study cohort from the Nationwide Inpatient Sample (NIS) for the years 2008–2017. Hospitalizations due to CVS were identified using International Classification of Diseases (9th/10th Editions) Clinical Modification diagnosis codes (ICD-9-CM/ICD-10-CM). Comorbidities were also identified by ICD-9/10-CM codes and Elixhauser comorbidity software. Our primary outcome was discharge to the facility following CVS hospitalization. We utilized multivariable survey logistic regression models to analyze the outcomes and identify predictors. RESULTS: A total 229,586 were patients hospitalized due to CVS during the study period. Number of hospitalizations due CVS decreased from 18,032 in 2008 to 17,420 in 2017. Among the hospitalised patients 24% were <18 years, 62% females and 66% caucasians. Mean Length of Stay (LOS) of hospitalized patients was 3.21 ± 0.02 days. Out of total hospitalizations, 5.75% were discharged to facilities and 0.29% died during hospitalization. Furthermore, in multivariable regression analysis, younger age <18 years (OR 2.0; 95% CI 1.6–2.5; P < 0.0001), Rural/non-teaching hospital (OR 2.2; 95% CI 1.9–2.5; P < 0.001) and Small bed size hospitals (OR 1.4; CI 1.3–1.6; P < 0.0001) and pre existing comorbidities such as Hypothyroidism (OR 1.1; 95% CI 1.0-1.3; P = 0.029), Psychiatric Disorders (OR 2.1; 95% CI 1.8–2.6; P < 0.0001) and depression (OR 1.2; 95% CI 1.1–1.4; P = 0.005) were associated with higher odds of discharge to the facility. Moreover in-hospital complications like septicemia, CHF, renal failure, were also associated with poor outcomes. However, patients with private insurance had decreased odds of discharge to a facility. CONCLUSION: We observed that hospitalisations due to CVS have declined over years. We delineated several predictors and comorbidities that have been associated with adverse outcomes, especially in the pediatric population. Efforts to mitigate comorbid conditions and modifiable predictors to reduce healthcare utilization are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".