Human disease clinical treatment network for the elderly: The analysis of medicare inpatient length of stay data

2021 ◽  
Author(s):  
Hao Mei ◽  
Ruofan Jia ◽  
Guanzhong Qiao ◽  
Zhenqiu Lin ◽  
Shuangge Ma
Keyword(s):  
Length Of Stay ◽  
Human Disease ◽  
The Elderly ◽  
Clinical Treatment ◽  
Treatment Network ◽  
Inpatient Length Of Stay

Clinical Treatment of the Elderly: Never Too Late

1987 ◽  
Vol 32 (12) ◽  
pp. 1036-1036
Author(s):  
Susan Krauss Whitbourne
Keyword(s):  
The Elderly ◽  
Clinical Treatment

scholarly journals Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anping Guo ◽  
Jin Lu ◽  
Haizhu Tan ◽  
Zejian Kuang ◽  
Ying Luo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Liver Disease ◽  
Length Of Stay ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospital Length ◽  
Clinical Treatment ◽  
Hospital Length Of Stay ◽  
Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

scholarly journals A Pay-for-Performance Initiative to Reduce Pediatric Psychiatric Inpatient Length of Stay

2019 ◽  
Vol 70 (2) ◽  
pp. 156-158 ◽  
Author(s):  
Timothy Schmutte ◽  
Laurie Van der Heide ◽  
Lori Szczygiel ◽  
Ann Phelan ◽  
Larry Davidson ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Pay For Performance ◽  
Psychiatric Inpatient ◽  
Inpatient Length Of Stay

Machine Learning in the Prediction of Medical Inpatient Length of Stay

2020 ◽  
Author(s):  
Stephen Bacchi ◽  
Yiran Tan ◽  
Luke Oakden‐Rayner ◽  
Jim Jannes ◽  
Timothy Kleinig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Medical Inpatient ◽  
Inpatient Length Of Stay

A single-center qualitative study evaluating the safety and efficacy of a pharmacist-driven protocol for high-dose methotrexate management

2020 ◽  
pp. 107815522092745
Author(s):  
Stephanie F Matta ◽  
Leslie A Gieselman ◽  
Robert S Mancini
Keyword(s):  
Length Of Stay ◽  
Kidney Injury ◽  
High Dose ◽  
High Dose Methotrexate ◽  
Improvement Project ◽  
Treatment Delays ◽  
Dose Methotrexate ◽  
Significant Difference ◽  
The Impact ◽  
Inpatient Length Of Stay

Introduction Delayed methotrexate clearance in several patients admitted to the oncology unit at a regional medical center necessitated the development of a pharmacist-driven protocol for supportive therapy with high-dose methotrexate. This performance improvement project evaluated the impact of the protocol on inpatient length of stay, patient safety, and clinical outcomes. Methods Retrospective data were collected over 14 months pre-implementation and prospective data were collected over 19 months post-implementation. Primary outcomes included mean length of stay and incidence of kidney injury. Secondary outcomes included myelosuppression, treatment delays, mucositis, protocol adherence, and pharmacist interventions. Chi-squared and unpaired two sample t-test were used for data analysis. Intervention A literature review of consensus recommendations for supportive care post high-dose methotrexate administration was conducted to develop the protocol. Education on implementation was provided to involved disciplines. Results One-hundred ten high-dose methotrexate admissions for 23 patients were analyzed: 24 pre-protocol and 86 post-protocol. Mean length of stay was 5.17 nights pre-protocol and 3.91 nights post-protocol ( p = 0.026). Incidence of kidney injury significantly decreased (16.7% pre-protocol versus 3.5% post-protocol; p = 0.0394). Lower incidences of all-grade anemia (83.3% versus 58.1%), neutropenia (62.5% versus 29.1%), and thrombocytopenia (58.3% versus 33.7%) as well as treatment delays (29.2% versus 11.6%; p = 0.036) were reported post protocol. No statistically significant difference in mucositis was detected. Pharmacist adherence to protocol was ≥80% resulting in 348 interventions with 99.4% provider acceptance. Conclusion The implementation of a pharmacist-driven high-dose methotrexate management protocol resulted in a statistically significant decrease in inpatient length of stay and kidney injury. Further studies are needed to assess the impact on additional outcomes.

scholarly journals Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance

Neurosurgery ◽  
2018 ◽  
Vol 85 (3) ◽  
pp. 384-393 ◽  
Author(s):  
Whitney E Muhlestein ◽  
Dallin S Akagi ◽  
Jason M Davies ◽  
Lola B Chambless
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Length Of Stay ◽  
Patient Outcomes ◽  
External Validation ◽  
Training Dataset ◽  
Ensemble Model ◽  
Improvement Program ◽  
Improve Patient ◽  
Inpatient Length Of Stay

Abstract BACKGROUND Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outcomes. Inpatient length of stay (LOS) is one such outcome that serves as a surrogate for patient disease severity and resource utilization. OBJECTIVE To develop a novel method to systematically rank, select, and combine ML algorithms to build a model that predicts LOS following craniotomy for brain tumor. METHODS A training dataset of 41 222 patients who underwent craniotomy for brain tumor was created from the National Inpatient Sample. Twenty-nine ML algorithms were trained on 26 preoperative variables to predict LOS. Trained algorithms were ranked by calculating the root mean square logarithmic error (RMSLE) and top performing algorithms combined to form an ensemble. The ensemble was externally validated using a dataset of 4592 patients from the National Surgical Quality Improvement Program. Additional analyses identified variables that most strongly influence the ensemble model predictions. RESULTS The ensemble model predicted LOS with RMSLE of .555 (95% confidence interval, .553-.557) on internal validation and .631 on external validation. Nonelective surgery, preoperative pneumonia, sodium abnormality, or weight loss, and non-White race were the strongest predictors of increased LOS. CONCLUSION An ML ensemble model predicts LOS with good performance on internal and external validation, and yields clinical insights that may potentially improve patient outcomes. This systematic ML method can be applied to a broad range of clinical problems to improve patient care.

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