The Barthel index as an indicator of hospital outcomes: A retrospective cross-sectional study with healthcare data from older people.

AIMS: The assessment of functional status is a more appropriate measure in the older people than traditional healthcare outcomes. The present study aimed to analyse the association between functional status assessed using the Barthel Index and length of stay, in-hospital mortality, discharge destination, and Diagnosis-Related Groups-based cost. DESIGN: This study was a retrospective study that used administrative data from patients older than 65 discharged from the University Hospital of Padua (Italy) in 2016. METHODS: A logistic regression model for categorical variables (length of stay, in-hospital mortality, and discharge destination) and a generalized linear model with gamma distributions and log links for continuous variables (cost of hospitalization) were used to evaluate associations with the Barthel Index. RESULTS: A total of 13,484 admissions were included in the analysis. In-hospital mortality, safe discharge, and length of stay were higher in patients with severe dependence than in patients with mild/no dependence with a 12-fold increased risk of death (OR = 12.81; 95% CI 9.22-18.14), a 4 times greater likelihood of safe discharge (OR = 4.64; 95% CI 3.96-5.45), and a 2-fold increase in length of stay (OR = 2.56; 95% CI 2.34-2.81). On the other hand, no significant association was found between the cost of hospitalization and the Barthel Index. CONCLUSIONS: Barthel Index was strongly associated with in-hospital mortality, discharge destination, and length of stay. The costs of hospitalization, however, were not related to patients' functional impairment. IMPACT: The study considers functional status as an indicator of hospital outcomes. Better comprehension of the relationship between functional status and healthcare outcomes may help with early and adequate healthcare planning and resource management.

The SYSTEMIC Project: A Pilot Study to Develop a Registry of Patients With an Ostomy for Predictive Modeling of Outcomes.

BACKGROUND: Stomal and peristomal skin complications represent a significant burden on the physical and psychological well-being of patients. PURPOSE: To develop a predictive tool for identifying the risk of complications in patients following ostomy surgery. METHODS: The oStomY regiSTry prEdictive ModelIng outCome (SYSTEMIC) project was developed to improve patient-oriented outcomes. Demographic, medical history, and stoma-related variables were obtained from patients at the wound ostomy clinic of the University Hospital of Padova, Italy. A follow-up assessment was completed 30 days after stoma surgery. Two (2) Bayesian machine learning approaches (naïve Bayes) were carried out to define an automatic peristomal complication predictive tool. A sensitivity analysis was performed to evaluate the possible effects of the prior choices on naïve Bayes performance. RESULTS: The algorithms were based on preliminary data from 52 patients (28 [53.3%] had a colostomy and 24 [46.7%] had an ileostomy). In terms of postoperative complications, no significant differences were observed between patients with different body mass indices (P = .16), those who underwent elective surgery compared with those who underwent emergency surgery (P = .66), and those who had or had not been preoperatively sited (P = .44). The algorithms showed an overall moderate ability to correctly classify patients according to the presence of peristomal complications (accuracy of nearly 70% in both models). In the the data-driven prior model, the probability of developing complications was greater for  participants with malignancies or other diseases (0.3314 for both levels) than for patients with diverticula and bowel perforation (0.1453) or inflammatory bowel disease (0.1918). CONCLUSION: The development of an easy-to-use algorithm may help nonspecialized nurses evaluate the likelihood of future peristomal complications in patients with an ostomy and implement preemptive measures.

A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome.

Delirium is a psycho-organic syndrome common in hospitalized patients, especially the elderly, and is associated with poor clinical outcomes. This study aims to identify the predictors that are mostly associated with the risk of delirium episodes using a machine learning technique (MLT). A random forest (RF) algorithm was used to evaluate the association between the subject's characteristics and the 4AT (the 4 A's test) score screening tool for delirium. RF algorithm was implemented using information based on demographic characteristics, comorbidities, drugs and procedures. Of the 78 patients enrolled in the study, 49 (63%) were at risk for delirium, 32 (41%) had at least one episode of delirium during the hospitalization (38% in orthopedics and 31% both in internal medicine and in the geriatric ward). The model explained 75.8% of the variability of the 4AT score with a root mean squared error of 3.29. Higher age, the presence of dementia, physical restraint, diabetes and a lower degree are the variables associated with an increase of the 4AT score. Random forest is a valid method for investigating the patients' characteristics associated with delirium onset also in small case-series. The use of this model may allow for early detection of delirium onset to plan the proper adjustment in healthcare assistance.

Profiling Delirium Progression in Elderly Patients via Continuous-Time Markov Multi-State Transition Models.

Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from no-delirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM.

Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning.

Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients' assistance needs.