Investigation of multiple nosocomial infections using a semi-Markov multi-state model

TL;DR

This study uses a semi-Markov model to analyze risk factors and impact of multiple hospital-acquired infections on patient hospital stays.

Contribution

A semi-Markov multi-state model is applied to quantify MNI risk factors and their impact on hospital length of stay.

Findings

MNI risk factors include age, community infection, surgery, and antibiotic use.

MNI occurs most frequently within 14 days of admission and prolongs hospital stays by 15.94 days on average.

More infection sites correlate with longer hospital stays for MNI patients.

Abstract

The prevalence of multiple nosocomial infections (MNIs) is on the rise, however, there remains a limited comprehension regarding the associated risk factors, cumulative risk, probability of occurrence, and impact on length of stay (LOS).

This multicenter study includes all hospitalized patients from 2020 to July 2023 in two sub-hospitals of a tertiary hospital in Guangming District, Shenzhen. The semi-Markov multi-state model (MSM) was utilized to analyze risk factors and cumulative risk of MNI, predict its occurrence probability, and calculate the extra LOS of nosocomial infection (NI).

The risk factors for MNI include age, community infection at admission, surgery, and combined use of antibiotics. However, the cumulative risk of MNI is lower than that of single nosocomial infection (SNI). MNI is most likely to occur within 14 days after admission. Additionally, SNI prolongs LOS by an average of 7.48 days (95% Confidence Interval, CI: 6.06–8.68 days), while MNI prolongs LOS by an average of 15.94 days (95% CI: 14.03–18.17 days). Furthermore, the more sites of infection there are, the longer the extra LOS will be.

The longer LOS and increased treatment difficulty of MNI result in a heavier disease burden for patients, necessitating targeted prevention and control measures.

The online version contains supplementary material available at 10.1186/s13756-024-01421-5.

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