Weekend Effect on in-Hospital Mortality for Ischemic and Hemorrhagic Stroke in US Rural and Urban Hospitals

Mekonnen, Birook; Wang, Guijing; Rajbhandari‐Thapa, Janani; Shi, Lu; Thapa, Kiran; Zhang, Zheng; Zhang, Donglan

doi:10.1016/j.jstrokecerebrovasdis.2020.105106

Cited by 9 publications

(12 citation statements)

References 37 publications

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“… 6 , 7 With an increasing number of patients with PAD admitted to the hospital, there is an urgent need to optimize treatment to improve clinical outcomes and reduce mortality for patients with PAD. The in‐hospital mortality in the United States among patients hospitalized with PAD was reported as being from 2.5% to 3.1% in the literature, 8 , 9 which was similar to in‐hospital mortality for patients with acute ischemic stroke (2.3% to 4.2%) 10 and slightly lower than that among patients with an acute myocardial infarction (4.6%). 11 Studies have reported that patients hospitalized for PAD with anemia, 12 chronic kidney disease, 13 atrial fibrillation, 14 and patients who underwent major lower extremity amputation 15 had higher risk for in‐hospital mortality.…”

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confidence: 61%

“…PAD is a major health care problem, and this condition has a high mortality during hospitalization when compared with ischemic stroke. 10 Using the NIS 2016 to 2019, we identified all patients admitted to the hospital primarily because of the PAD diagnosis. ML models were established to predict in‐hospital mortality and reveal important risk factors.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States

Zhang

Kalbaugh

et al. 2022

JAHA

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Background Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in‐hospital mortality in patients hospitalized for PAD based on a national database. Methods and Results Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD‐related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification ( ICD‐10‐CM ) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD‐10‐PCS) . Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in‐hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital‐related factors. In‐hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision‐making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in‐hospital mortality. Conclusions This study demonstrates the feasibility of ML in predicting in‐hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high‐risk patients for poor outcomes and guiding personalized intervention.

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confidence: 61%

Section: Discussionmentioning

confidence: 99%

Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States

Zhang

Kalbaugh

et al. 2022

JAHA

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“…For more than 20 years, the term “weekend effect” has been used by the medical community to describe the higher mortality rate for hospitalizations occurring on weekends versus weekdays [ 3 ]. This effect has been recognized in the fields of cancer, pulmonary embolism, and stroke [ 2 , 13 ]. Several studies examined the weekend effect in ischemic stroke requiring endovascular MT.…”

Section: Discussionmentioning

confidence: 99%

Differences in mechanical thrombectomy for acute ischemic stroke on weekdays versus nights/ weekends in a Japanese primary stroke core center

Omura¹,

Kakita²,

Fukuo³

et al. 2023

J Cerebrovasc Endovasc Neurosurg

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Objective: The term “weekend effect” refers to an increase in the mortality rate for hospitalizations occurring on weekends versus weekdays. In this study, we investigated whether such an effect exists in patients undergoing mechanical thrombectomy for acute ischemic stroke with large vessel occlusion (currently the standard treatment for this condition) at a single center in Japan.Methods: We surveyed 151 patients who underwent mechanical thrombectomy for acute ischemic stroke with large vessel occlusion (75 and 76 patients were treated during daytime and nighttime, respectively) from January 2019 to June 2021. The items evaluated in this analysis were the rate of modified Rankin Scale ≤2 or prestroke scale, mortality, and procedural treatment time.Results: The rates of modified Rankin Scale ≤2 or prestroke scale and mortality at 90 days after treatment did not differ significantly between daytime and nighttime (41.3% vs. 29.0%, p=0.11; 14.7% vs. 11.8%, p=0.61, respectively). The door-to-groin time tended to be shorter during daytime versus nighttime (57 [IQR: 42.5–70] min vs. 70 [IQR: 55–82]) min, p=0.0507).Conclusions: This study did not reveal differences in treatment outcome between daytime and nighttime in patients undergoing mechanical thrombectomy for acute ischemic stroke with large vessel occlusion. Therefore, the “weekend effect” was not observed in our institution.

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“…Based on 2016 National Inpatient Sample data, a United States study did not observe a relationship between mortality and admission day among AIS patients with rural–urban diversity, which was attributed to updated investigations and the efforts of modern stroke care quality promotion ( 31 ). A study reported that rural AIS patients treated in hospitals in agricultural regions had similar benefits and safety from thrombolytic treatment in Portland and even had a lower chance of intravenous thrombolysis at night, and improvements in telecommunication and patient transport from rural areas during the night weakened the weekend effect ( 47 ).…”

Section: Discussionmentioning

confidence: 99%

Effect of weekend versus weekday admission on the mortality of acute ischemic stroke patients in China: an analysis of data from the Chinese acute ischemic stroke treatment outcome registry

Huang

Sun

et al. 2023

Front. Neurol.

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BackgroundDue to disparities in medical resources in rural and urban areas as well as in different geographic regions in China, the effect of weekend versus weekday admission on the outcomes of acute ischemic stroke (AIS) patients is unknown. Our aim was to investigate whether the outcomes of AIS patients differ according to the day of admission in China.MethodsThe data were extracted from the Chinese Acute Ischemic Stroke Treatment Outcome Registry (CASTOR), a multicenter prospective study database of patients diagnosed with AIS. The chi-square test (χ2) and logistic regression were used to assess mortality for weekday and weekend admissions among AIS patients stratified by rural or urban status and geographic region (including the eastern, northeastern, central, and western regions).ResultsIn total, 9,256 patients were included in this study. Of these patients, 57.2% were classified as urban, and 42.8% were classified as rural. A total of 6,760 (73%) patients were admitted on weekdays, and 2,496 (27%) were admitted on weekends. There was no significant difference in the mortality rate among patients admitted on weekends compared with those admitted on weekdays in urban (7.5% versus 7.4%) or rural areas (8.8% versus 8.1%; p > 0.05). The mortality rate was the highest among patients admitted on weekends and weekdays (11.6% versus 10.3%) in the northeastern area, without statistical significance before and after adjusting for the patients’ background characteristics (p > 0.05). In addition, regression analysis revealed that the mortality of patients admitted on weekdays was more likely to be influenced by regional subgroup, hospital level and intravenous thrombolysis than that of patients admitted on weekends.ConclusionThe weekend effect was not observed in the mortality of patients with AIS regardless of rural–urban status or geographic region in China.

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Weekend Effect on in-Hospital Mortality for Ischemic and Hemorrhagic Stroke in US Rural and Urban Hospitals

Cited by 9 publications

References 37 publications

Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States

Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States

Differences in mechanical thrombectomy for acute ischemic stroke on weekdays versus nights/ weekends in a Japanese primary stroke core center

Effect of weekend versus weekday admission on the mortality of acute ischemic stroke patients in China: an analysis of data from the Chinese acute ischemic stroke treatment outcome registry

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