(104) PREDICTORS OF LENGTH OF STAY IN PELVIC INFLAMMATORY DISEASE HOSPITALIZATION: INSIGHTS FROM THE NATIONAL INPATIENT SAMPLE
Authors: Derakshani D , Dodoo C , Sobel T 
Published: June 2024

Introduction

Pelvic inflammatory disease (PID) is a common and potentially serious reproductive health condition that may require hospitalization for proper management. Accurate prediction of the length of stay (LOS) for PID hospitalizations can aid clinicians in resource allocation, patient counseling, and improving overall patient care. Prior studies have explored potential risk factors for a poor clinical course among women hospitalized with PID, but to our knowledge, no study to date has explored potential predictors of LOS in PID hospitalization.

Objective

This study aims to characterize the predictors for LOS in PID hospitalizations based on patient and hospital characteristics using a large national database.

Methods

The National Inpatient Sample (NIS) database was queried from 2019 to 2020 for all female patients 18 years and older with a primary diagnosis of acute pelvic inflammatory disease using ICD-10 codes. Data on patient demographics, clinical characteristics, comorbidities, and hospital-related variables were collected using variables as defined by NIS and summarized using descriptive statistics. Bivariate and multivariable associations between LOS and covariates were assessed using linear regression models and estimates were reported as regression coefficients. Covariates that were statistically significant in the bivariate models were included as covariates during the multivariate regression analysis.

Results

A total of 14,505 hospitalized female patients with a principal diagnosis code for acute PID were included. Mean LOS for PID hospitalization was 5.8 days, with a range of 0 – 88 days. The average patient age was 42.9 years (age range 18 – 90 years old), with 55.1% White, 23.4% Black, 12.5% Hispanic, and 2.3% Asian or Pacific Islander. The main primary payers included Medicaid (40.0%), private insurance (36.1%), Medicare (19.1%), and self-pay (4.7%). About 34% of patients were in the lowest quartile for median household income, 45% in the middle two quartiles, and 19% in the highest quartile. In univariable analysis, increased LOS was associated with increasing age, larger hospital bed size, urban teaching hospital status, and the presence of hypertension, diabetes, obesity, or immunodeficiency. Decreased LOS was associated with Hispanic or Native American race and having a non-Medicare primary payer. In multivariable analysis, increased LOS was associated with increasing age (+0.06 days per increasing year, p &lt; 0.0001) and large hospital bed size (+0.6 days compared to small bed size, p = 0.03). Decreased LOS was associated with Asian or Pacific Islander race (-1.1 days compared to White race, p = 0.02), Native American race (-1.4 days compared to White race, p = 0.03), and being billed as ‘no charge’ (-2.1 days compared to Medicare, p = 0.03).

Conclusions

This study identifies possible predictors of LOS in PID hospitalization, providing insights that may help optimize resource allocation and better characterize factors that may impact PID management. Several factors, including age, race, expected primary payer, and bed size of hospital were found to significantly impact LOS. Additionally, this study highlights the potential role of social determinants of health in PID hospitalization and management and can help guide future research and initiatives aimed at improving population health and addressing disparities in PID.

Disclosure

No.