Psychiatric Morbidity and Impact on Hospital Length of Stay Among Hematologic Cancer Patients Receiving Stem-Cell Transplantation

2002 ◽  
Vol 20 (7) ◽  
pp. 1907-1917 ◽  
Author(s):  
Jesús M. Prieto ◽  
Jordi Blanch ◽  
Jorge Atala ◽  
Enric Carreras ◽  
Montserrat Rovira ◽  
...  
Keyword(s):  
Stem Cell ◽  
Length Of Stay ◽  
Stem Cell Transplantation ◽  
Hospital Admission ◽  
Psychiatric Disorders ◽  
Psychiatric Morbidity ◽  
Adjustment Disorder ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Hematologic Cancer

PURPOSE: To determine the prevalence of psychiatric disorders during hospitalization for hematopoietic stem-cell transplantation (SCT) and to estimate their impact on hospital length of stay (LOS). PATIENTS AND METHODS: In a prospective inpatient study conducted from July 1994 to August 1997, 220 patients aged 16 to 65 years received SCT for hematologic cancer at a single institution. Patients received a psychiatric assessment at hospital admission and weekly during hospitalization until discharge or death, yielding a total of 1,062 psychiatric interviews performed. Psychiatric disorders were determined on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Univariate and multivariate linear regression analyses were used to identify variables associated with LOS. RESULTS: Overall psychiatric disorder prevalence was 44.1%; an adjustment disorder was diagnosed in 22.7% of patients, a mood disorder in 14.1%, an anxiety disorder in 8.2%, and delirium in 7.3%. After adjusting for admission and in-hospital risk factors, diagnosis of any mood, anxiety, or adjustment disorder (P = .022), chronic myelogenous leukemia (P = .003), Karnofsky performance score less than 90 at hospital admission (P = .025), and higher regimen-related toxicity (P < .001) were associated with a longer LOS. Acute lymphoblastic leukemia (P = .009), non-Hodgkin’s lymphoma (P = .04), use of peripheral-blood stem cells (P < .001), second year of study (P < .001), and third year of study (P < .001) were associated with a shorter LOS. CONCLUSION: Our data indicate high psychiatric morbidity and an association with longer LOS, underscoring the need for early recognition and effective treatment.

scholarly journals Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bindu Vekaria ◽  
Christopher Overton ◽  
Arkadiusz Wiśniowski ◽  
Shazaad Ahmad ◽  
Andrea Aparicio-Castro ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hospital Admission ◽  
Failure Time ◽  
National Level ◽  
Hospital Length ◽  
Survival Model ◽  
Data Driven ◽  
Hospital Length Of Stay ◽  
Similar Data ◽  
Model Complex

Abstract Background Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. Conclusions Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

scholarly journals Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning

2020 ◽  
Author(s):  
Bindu Vekaria ◽  
Christopher Overton ◽  
Arkadiusz Wisniowski ◽  
Shazaad Ahmad ◽  
Andrea Aparicio-Castro ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hospital Admission ◽  
Failure Time ◽  
National Level ◽  
Hospital Length ◽  
Survival Model ◽  
Data Driven ◽  
Hospital Length Of Stay ◽  
Similar Data ◽  
Model Complex

Abstract Background: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 9.3 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 22.8 days. Conclusions: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

scholarly journals 892. Meningitis after Ventricular Shunt Operations: Multicenter Study to Identify Etiology, Incidence and Risk Factors

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S481-S481
Author(s):  
Lucca G Giarola ◽  
Handerson Dias Duarte de Carvalho ◽  
Braulio Roberto Gonçalves Marinho Couto ◽  
Carlos Ernesto Ferreira Starling
Keyword(s):  
Risk Factors ◽  
Length Of Stay ◽  
Hospital Admission ◽  
Hospital Stay ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Hospital Death ◽  
Communicating Hydrocephalus ◽  
Increased Risk ◽  
Belo Horizonte

Abstract Background A Ventriculoperitoneal shunt is the main treatment for communicating hydrocephalus. Surgical site infection associated with the shunt device is the most common complication and an expressive cause of morbidity and mortality of the treatment. The objective of our study is to answer three questions: a)What is the risk of meningitis after ventricular shunt operations? b) What are the risk factors for meningitis? c) What are the main microorganisms causing meningitis? Methods A retrospective cohort study assessed meningitis and risk factors in patients undergoing ventricular shunt operations between 2015/Jul and 2018/Jun from 12 hospitals at Belo Horizonte, Brazil. Data were gathered by standardized methods defined by the National Healthcare Safety Network (NHSN)/CDC procedure-associated protocols for routine SSI surveillance. Sample size = 926. 26 variables were evaluated by univariate and multivariate analysis (logistic regression). Results 71 patients were diagnosed with meningitis which represent a risk of 7.7% (C.I.95%= 6.1%; 9.6%). From the 26 variables, three were acknoleged as risk factors: age &lt; two years old (OR = 3.20; p &lt; 0.001), preoperative hospital length of stay &gt; four days (OR = 2.02; p = 0.007) and more than one surgical procedure (OR = 3.23; p = 0.043). Patients two or more years old, who had surgery four days after hospital admission, had increased risk of meningitis from 4% to 6% (p = 0.140). If a patient &lt; two years had surgery four days post hospital admission, the risk is increased from 9% to 18% (p = 0.026). 71 meningitis = &gt; 45 (63%) the etiologic agent identified: Staphylococcus aureus (33%), Staphylococcus epidermidis (22%), Acinetobacter sp (7%), Enterococcus sp (7%), Pseudomonas sp (7%), and other (18%). Hospital length of stay in non-infected patients (days): mean = 21 (sd = 28), median = 9; hospital stay in infected patients: mean = 34 (sd = 37), median = 27 (p=0.025). Mortality rate in patients without infection was 10% while hospital death of infected patients was 13% (p=0.544). Conclusion Two intrinsic risk factors for meningitis post ventricular shunt, age under two years old and multiple surgeries, and one extrinsic risk factor, preoperative length of hospital stay, were identified. Incidence of meningitis post VP shunt decreases with urgent surgical treatment. Disclosures All Authors: No reported disclosures

scholarly journals Prevalence of Hospital Admissions for Low Back Pain: Protocol for a Systematic Review and Meta-Analysis

2020 ◽  
Author(s):  
Harrison J Lord ◽  
Danielle Coombs ◽  
Christopher Maher ◽  
Gustavo C Machado
Keyword(s):  
Emergency Department ◽  
Low Back Pain ◽  
Back Pain ◽  
Length Of Stay ◽  
Hospital Admission ◽  
Hospital Admissions ◽  
Functional Decline ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Low Back

Low back pain is the leading cause of years lived with disability in most countries and creates a huge burden for healthcare systems globally. Around the globe, 4.4% of all emergency department attendances are attributed to low back pain, and subsequent admissions to hospital seem to be common. These hospitalisations can result in unnecessary medical care, functional decline and high costs. There are no systematic reviews summarising the global prevalence of hospital admission for low back pain, identifying the sources of admissions or estimating hospital length of stay. This information would be valuable for health and medical researchers, front-line clinicians, and health planners aiming to improve and increase the value of their health services. The objectives of this study are to estimate the prevalence of hospital admission for low back pain from different healthcare facilities across the globe, including the emergency department, as well as investigate hospital length of stay and explore sources of heterogeneity when categorising studies according to low back pain definitions, sources of admission, study period, study setting and country’s region and income level.

Hospital Length of Stay in Individuals With Schizophrenia With and Without Cocaine-Positive Urine Drug Screens at Hospital Admission

2015 ◽  
Vol 203 (1) ◽  
pp. 33-36 ◽  
Author(s):  
Hanjing Emily Wu ◽  
Satyajit Mohite ◽  
Ikenna Ngana ◽  
Wilma Burns ◽  
Nurun Shah ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hospital Admission ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Positive Urine ◽  
Urine Drug
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