Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study

Abstract

Background Healthcare associated infections (HAI) are a major burden for the healthcare system and associated with prolonged hospital stay, increased morbidity, mortality and costs. Healthcare associated urinary tract infections (HA-UTI) accounts for about 20–30% of all HAI’s, and with the emergence of multi-resistant urinary tract pathogens, the total burden of HA-UTI will most likely increase. Objective The aim of the current study was to develop two predictive models, using data from the index admission as well as historic data on a patient, to predict the development of UTI at the time of entry to the hospital and after 48 hours of admission (HA-UTI). The ultimate goal is to predict the individual patient risk of acquiring HA-UTI before it occurs so that health care professionals may take proper actions to prevent it. Methods Retrospective cohort analysis of approx. 300 000 adult admissions in a Danish region was performed. We developed models for UTI prediction with five machine-learning algorithms using demographic information, laboratory results, data on antibiotic treatment, past medical history (ICD10 codes), and clinical data by transformation of unstructured narrative text in Electronic Medical Records to structured data by Natural Language Processing. Results The five machine-learning algorithms have been evaluated by the performance measures average squared error, cumulative lift, and area under the curve (ROC-index). The algorithms had an area under the curve (ROC-index) ranging from 0.82 to 0.84 for the entry model (T = 0 hours after admission) and from 0.71 to 0.77 for the HA-UTI model (T = 48 hours after admission). Conclusion The study is proof of concept that it is possible to create machine-learning models that can serve as early warning systems to predict patients at risk of acquiring urinary tract infections during admission. The entry model and the HA-UTI models perform with a high ROC-index indicating a sufficient sensitivity and specificity, which may make both models instrumental in individualized prevention of UTI in hospitalized patients. The favored machine-learning methodology is Decision Trees to ensure the most transparent results and to increase clinical understanding and implementation of the models.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

“…The Machine Learning models described incorporated a variety of data inputs from patients in multiple different settings, leveraged several distinct algorithm types, and varied in the composition of stakeholder involvement. AI System Development of the predictive models included data from EHRs and/or administrative databases [22][23][24][25][26][27][28][29][30][31] as well as prospectively collected data such as physiological data from wearables 32 and imaging data 24 . Vital signs, laboratory values, diagnoses, and clinical notes were common sources of input data from the EHR.…”

“…While five studies did not report the expertise of personnel involved in model development, other papers reported clinicians, (e.g., nurses, physicians, occupational therapists, case managers) 23,24,27,32 data scientists 26 , statisticians 24 , informaticians 25 , policymakers 26 , and un-specified hospital staff 26,32 as being involved in model development in some capacity. The nursing-relevant outcomes targeted by the data science systems fell within three clinical domains: mortality/deterioration 22,24,25,32 , health care utilization/resource allocation 23,26,27,29,30 , and hospital acquired infections/COVID-19 28,31 .…”

“…Some studies also used more general terms to describe team membership such as hospital stakeholders, hospital staff, or subject matter experts so the roles of some team members could not be specifically identified 23,26,30 . In some studies, there was no explicit report of project team membership 22,28,31,32 . Due to the use of general term or omission of team membership, it is difficult to ascertain whether the target users of the systems were included in the implementation teams.…”

“…The nursing-relevant outcomes targeted by the data science systems fell within three clinical domains: mortality/deterioration, 22,24,25,32 health care utilization/resource allocation, 23,26,27,29,30 and hospital-acquired infections/COVID-19. 28,31…”

Explainable Artificial Intelligence (XAI) Model for the Diagnosis of Urinary Tract Infections in Emergency Care Patients

“…Recently, Møller et al [21] developed a preemptive UTI diagnosis system to identify the patients at risk during the hospitalization. Several ML models have been used such as NN, DT, GB, and regression model to predict the hospital acquired and catheter acquired UTI at the time of admission and after 48 hours.…”