Determinants of Prolonged Length of Hospital Stay in Patients with Severe Acute Ischemic Stroke

Abstract

Objective: Long hospitalizations are associated with a high comorbidity and considerable hospital cost. Admissions of severe acute ischemic stroke are prone to longer hospitalizations. We aimed to explore the issue and method for improving the length of stay. Methods: From the prospective Stroke Registry between January 2019 and June 2020, acute ischemic strokes with an admission National Institutes of Health Stroke Scale ≥ 15 were identified. Prolonged length-of-stay was defined as in-hospital-stay ≥ 30 days. All clinical characteristics were collected, and all do-not-resuscitate documentations were categorized if the order had been written within 7 days of onset. Results: A total of 212 patients were eligible for severe stroke. Of these, 42 (19.8%) had prolonged length-of-stay and 170 had non-prolonged length-of-stay (median 43 vs. 13 days). The prolonged group was younger, mostly men, and was more likely to be in an independent state and more likely to receive reperfusion therapy, and there was a higher frequency of late do-not-resuscitate orders if signed. Although there was a lower in-hospital mortality rate in the prolonged group (12% vs. 23%), there was a higher proportion with a severe functional state (Modified Rankin Scale = 4–5) among the survivors (97% vs. 87%). Conclusions: Severe acute ischemic stroke patients with a prolonged length-of-stay were younger, mostly male, more likely to receive reperfusion therapy, less likely to have an early do-not-resuscitate order if signed, and more likely to have poor functional status at discharge, although there was a lower rate of in-hospital mortality.

Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks

“…Prolonged hospitalization days are inevitably accompanied by an increase in medical costs, such as the consumption of beds, medications, care, and health resources. Complications associated with prolonged LOS negatively impact the prognosis and quality of life of patients with AIS and may increase the drain on medical resources and the financial burden on patients ( 17 , 19 , 20 ). Therefore, studies such as the present one identifying the independent predictors of LOS and developing reliable prediction methods are warranted and essential to provide data for improved decision-making related to disease management, care resource allocation, and prediction of treatment cost-effectiveness, consequently contributing positively to the individual or national healthcare management budgeting ( 14 ).…”

“…Evidence exists that prolonged LOS is associated with poor functional outcomes and unfavorable discharge disposition in patients with AIS ( 17 , 40 ). This is in concordance with current evidence that patients with a prolonged LOS exhibited a 9.3-fold higher risk of unfavorable AIS outcomes at discharge than their non-prolonged LOS counterparts.…”

“…In addition to hospitalization costs, prolonged LOS may be associated with the increased use of healthcare resources, negatively affecting hospital capacity and medical personnel availability, reducing the quality of care, and preventing other patients from receiving inpatient care ( 15 , 16 ). Furthermore, patients with acute ischemic stroke (AIS) having prolonged LOS are more likely to develop complications such as hospital-acquired infections or gastrointestinal bleeding ( 17 , 18 ). Prolonged LOS is also associated with poststroke depression and increased disability, negatively affecting patients' quality of life ( 19 , 20 ).…”

“…Prolonged LOS is also associated with poststroke depression and increased disability, negatively affecting patients' quality of life ( 19 , 20 ). Prolonged LOS after AIS was also reported to be associated with less favorable neurological outcomes at hospital discharge ( 17 ). Therefore, acquiring knowledge of the factors associated with LOS and estimating the likelihood of prolonged LOS enable the prediction of recovery from an AIS episode and are crucial for clinical applications.…”

Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients

“…Previous studies have developed algorithms for predicting the LOS focused on disease-speci c surgeries [44][45][46][47] . Predicting the risk factors of critically ill patients is signi cant since a prolonged stay for patients can increase the risk of hospital-acquired infections and hinder other patients' access to the operation and medical resources 48 .…”