scholarly journals An interactive tool to forecast US hospital needs in the coronavirus 2019 pandemic

JAMIA Open ◽  
2020 ◽  
Author(s):  
Kenneth J Locey ◽  
Thomas A Webb ◽  
Jawad Khan ◽  
Anuja K Antony ◽  
Bala Hota
Keyword(s):  
Length Of Stay ◽  
Protective Equipment ◽  
Time Lags ◽  
Hospital Level ◽  
Home Institution ◽  
Hospital Beds ◽  
Forecast Data ◽  
Hospital Visits ◽  
Carry Over ◽  
Continuous Use

Abstract Objective We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. Materials and Methods Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from 7 COVID-19 models, customize 23 parameters, examine trends in testing and hospitalization, and download forecast data. Results Our application accurately predicts the spread of COVID-19 across states and territories. Its hospital-level forecasts are in continuous use by our home institution and others. Discussion Our application is versatile, easy-to-use, and can help hospitals plan their response to the changing dynamics of COVID-19, while providing a platform for deeper study. Conclusion Empowering healthcare responses to COVID-19 is as crucial as understanding the epidemiology of the disease. Our application will continue to evolve to meet this need.

scholarly journals An Interactive Tool to Forecast US Hospital Needs in the Coronavirus 2019 Pandemic

2020 ◽  
Author(s):  
Kenneth J. Locey ◽  
Thomas A. Webb ◽  
Jawad Khan ◽  
Anuja K. Antony ◽  
Bala Hota
Keyword(s):  
Open Source ◽  
Web Application ◽  
Healthcare Workers ◽  
Protective Equipment ◽  
Large Set ◽  
Specialized Knowledge ◽  
Web Based ◽  
Us States ◽  
Forecast Data ◽  
Hospital Visits

ABSTRACTHospital enterprises are currently faced with anticipating the spread of COVID-19 and the effects it will have on visits, admissions, bed needs, and crucial supplies. While many studies have focused on understanding the basic epidemiology of the disease, few open source tools have been made available to aid hospitals in their planning. We developed a web-based application (available at: http://covid19forecast.rush.edu/) for US states and territories that allows users to choose from a suite of models already employed in characterizing the spread of COVID-19. Users can obtain forecasts for hospital visits and admissions as well as anticipated needs for ICU and non-ICU beds, ventilators, and personal protective equipment supplies. Users can also customize a large set of inputs, view the variability in forecasts over time, and download forecast data. We describe our web application and its models in detail and provide recommendations and caveats for its use. Our application is primarily designed for hospital leaders, healthcare workers, and government official who may lack specialized knowledge in epidemiology and modeling. However, specialists can also use our open source code as a platform for modification and deeper study. As the dynamics of COVID-19 change, our application will also change to meet emerging needs of the healthcare community.

scholarly journals Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Grosso Francesca Maria ◽  
Presanis Anne Margaret ◽  
Kunzmann Kevin ◽  
Jackson Chris ◽  
Corbella Alice ◽  
...  
Keyword(s):  
Cohort Study ◽  
Length Of Stay ◽  
Median Length ◽  
Calendar Time ◽  
Fatality Risk ◽  
Lengths Of Stay ◽  
Hospital Beds ◽  
Hospital Patients ◽  
Poor Outcomes ◽  
Emergency Measures

Abstract Background The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February–June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February–June 2020, with non-missing hospital of admission and non-missing admission date. Results The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56–80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1–28.0%); and steadily decreased from 34.6% (32.5–36.6%) in February to 7.6% (6.3–10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6–12.3) days, compared to 8.1 (7.8–8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5–22.8) days in February to 5.2 (4.7–5.8) days in June. Conclusions The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2 i.

scholarly journals Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yong Chen
Keyword(s):  
Length Of Stay ◽  
Prediction Performance ◽  
Gradient Boosting ◽  
Distribution Law ◽  
Effective Technique ◽  
Weighting Method ◽  
Imbalance Data ◽  
Extreme Gradient Boosting ◽  
Hospital Beds ◽  
Log Likelihood

An improved nonlinear weighted extreme gradient boosting (XGBoost) technique is developed to forecast length of stay for patients with imbalance data. The algorithm first chooses an effective technique for fitting the duration of stay and determining the distribution law and then optimizes the negative log likelihood loss function using a heuristic nonlinear weighting method based on sample percentage. Theoretical and practical results reveal that, when compared to existing algorithms, the XGBoost method based on nonlinear weighting may achieve higher classification accuracy and better prediction performance, which is beneficial in treating more patients with fewer hospital beds.

Comparison of Risk-Adjusted Outcomes in Medicare Open versus Laparoscopic Cholecystectomy

2018 ◽  
Vol 84 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Donald E. Fry ◽  
Michael Pine ◽  
Susan M. Nedza ◽  
Agnes M. Reband ◽  
Chun-Jung Huang ◽  
...  
Keyword(s):  
Laparoscopic Cholecystectomy ◽  
High Risk ◽  
Length Of Stay ◽  
Adverse Outcome ◽  
Adverse Outcomes ◽  
High Risk Population ◽  
Hospital Level ◽  
Prolonged Length ◽  
Medicare Patients ◽  
Open Versus

More than 90 per cent of cholecystectomies are performed laparoscopically and this has resulted in concern that surgeons will not have sufficient experience to perform open procedures when clinical circumstances require it. We reviewed the open cholecystectomies (OCs) of Medicare patients from 2010 to 2012 in hospitals with 20 or more cases, created risk-adjusted models for adverse outcomes which were evaluated for 90-days after discharge, and compared the hospital-level outcomes with laparoscopic cholecystectomy performed in the same hospitals for the same period of time. Results demonstrated that inpatient deaths, inpatient prolonged length-of-stay outliers, 90-day postdischarge deaths without readmission, and 90-day readmissions were statistically the same with an overall adverse outcome rate of 21.6 per cent in OC versus 20.9 per cent in laparoscopic cholecystectomy. Conversion of laparoscopic to open procedures was not associated with increased adverse outcomes. Laparoscopic cholecystectomy provides patients with many advantages, but when clinical circumstances are necessary, OC continues to be performed with the same overall adverse outcome rates, and the conversion process is not associated with poorer results in this high-risk population of patients.

scholarly journals Changes in Emergency Department Responses Between SARS and COVID-19 in Taiwan

2020 ◽  
Author(s):  
Chia Wei Lin ◽  
Kuan-Ho Lin ◽  
Hong-Mo Shih ◽  
Kai-Wei Yang ◽  
Kao-Pin Hwang ◽  
...  
Keyword(s):  
Emergency Department ◽  
Rating Scale ◽  
Early Stage ◽  
Control Measures ◽  
Protective Equipment ◽  
Policy Reforms ◽  
Policy Changes ◽  
Hospital Level ◽  
Fever Patient ◽  
Infection Control Measures

Abstract Background: After SARS outbreak, infectious control polices had been reformed in Taiwan, but there was no evidence to prove its effectiveness. This study compared emergency department (ED) responses to the SARS and COVID-19 epidemics and investigate how policy changes affect infection prevention.Methods: A 2003 questionnaire regarding the responses of EDs to SARS was resent to EDs during the COVID-19 epidemic in 2020. The use of personal protective equipment (PPE), implementation of infection control measures (ICMs), and difficulties in performance were compared. Data collection included hospital level, different PPE types provided and ICMs implemented, timing for using PPE and ICMs, and a difficulty rating scale for ICM implementation.Results: In total, 100 EDs responded to the questionnaire in 2003 was reviewed and compared with 131 EDs in 2020. In COVID-19 epidemic, the use of basic PPEs and ICMs was mostly significantly improved, but the percentage of preparedness in high grade PPEs was still low. Quarantine of fever patients outside of EDs was less performed in small to medium sized hospitals (p=0.008 and 0.004). All of the additional ICMs were significantly less implemented in local hospitals. The timing to use PPE and implement ICMs were simultaneously and significantly performed at early stage. Instituting a fever triage ward and restricting fever patient admission became less necessary. The closure of EDs remained the most difficult to perform in both outbreaks. Conclusion: After the policy reforms, ED responses became earlier and more consistent. However, inadequate quarantine resources at EDs in low- and middle-grade hospitals may lead to breaches in future epidemics.

scholarly journals THE EFFECT OF A BOARDING RESTRICTION PROTOCOL ON EMERGENCY DEPARTMENT CROWDING

2021 ◽  
Author(s):  
Ji Hwan Lee ◽  
Ji Hoon Kim ◽  
Incheol Park ◽  
Hyun Sim Lee ◽  
Joon Min Park ◽  
...  
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Primary Outcome ◽  
Treatment Time ◽  
Patient Flow ◽  
Hospital Beds ◽  
Before And After ◽  
Day Of The Week ◽  
Ed Crowding ◽  
Post Period

ABSTRACT Background Access block due to a lack of hospital beds causes emergency department (ED) crowding. We initiated the boarding restriction protocol that limits ED length of stay (LOS) for patients awaiting hospitalization to 24 hours from arrival. This study aimed to determine the effect of the protocol on ED crowding. Method This was a pre-post comparative study to compare ED crowding before and after protocol implementation. The primary outcome was the red stage fraction with more than 71 occupying patients in the ED (severe crowding level). LOS in the ED, treatment time and boarding time were compared. Additionally, the pattern of boarding patients staying in the ED according to the day of the week was confirmed. Results Analysis of the number of occupying patients in the ED, measured at 10-minute intervals, indicated a decrease from 65.0 (51.0-79.0) to 55.0 (43.0-65.0) in the pre- and post-periods, respectively (p<0.0001). The red stage fraction decreased from 38.9% to 15.1% of the pre- and post-periods, respectively (p<0.0001). The proportion beyond the goal of this protocol of 24 hours decreased from 7.6% to 4.0% (p<0.0001). The ED LOS of all patients was similar: 238.2 (134.0-465.2) and 238.3 (136.9-451.2) minutes in the pre- and post-periods, respectively. In admitted patients, ED LOS decreased from 770.7 (421.4-1587.1) to 630.2 (398.0-1156.8) minutes (p<0.0001); treatment time increased from 319.6 (198.5-482.8) to 344.7 (213.4-519.5) minutes (p<0.0001); and boarding time decreased from 298.9 (109.5-1149.0) to 204.1 (98.7-545.7) minutes (p<0.0001). In the pre-period, boarding patients accumulated in the ED on weekdays, with the accumulation resolved on Fridays; this pattern was alleviated in the post-period. Conclusions The protocol effectively resolved excessive ED crowding by alleviating the accumulation of boarding patients in the ED on weekdays. Additional studies should be conducted on changes this protocol brings to patient flow hospital-wide.

scholarly journals Does service heterogeneity have an impact on acute hospital length of stay in stroke? A UK-based multicentre prospective cohort study

BMJ Open ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. e024506 ◽  
Author(s):  
Michelle Tørnes ◽  
David McLernon ◽  
Max Bachmann ◽  
Stanley Musgrave ◽  
Elizabeth A Warburton ◽  
...  
Keyword(s):  
Cohort Study ◽  
Length Of Stay ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Multiple Linear Regression Model ◽  
Hospital Length ◽  
Acute Hospital ◽  
Hospital Length Of Stay ◽  
Secondary Outcome ◽  
Hospital Level

ObjectivesTo determine whether stroke patients’ acute hospital length of stay (AHLOS) varies between hospitals, over and above case mix differences and to investigate the hospital-level explanatory factors.DesignA multicentre prospective cohort study.SettingEight National Health Service acute hospital trusts within the Anglia Stroke & Heart Clinical Network in the East of England, UK.ParticipantsThe study sample was systematically selected to include all consecutive patients admitted within a month to any of the eight hospitals, diagnosed with stroke by an accredited stroke physician every third month between October 2009 and September 2011.Primary and secondary outcome measuresAHLOS was defined as the number of days between date of hospital admission and discharge or death, whichever came first. We used a multiple linear regression model to investigate the association between hospital (as a fixed-effect) and AHLOS, adjusting for several important patient covariates, such as age, sex, stroke type, modified Rankin Scale score (mRS), comorbidities and inpatient complications. Exploratory data analysis was used to examine the hospital-level characteristics which may contribute to variance between hospitals. These included hospital type, stroke monthly case volume, service provisions (ie, onsite rehabilitation) and staffing levels.ResultsA total of 2233 stroke admissions (52% female, median age (IQR) 79 (70 to 86) years, 83% ischaemic stroke) were included. The overall median AHLOS (IQR) was 9 (4 to 21) days. After adjusting for patient covariates, AHLOS still differed significantly between hospitals (p<0.001). Furthermore, hospitals with the longest adjusted AHLOS’s had predominantly smaller stroke volumes.ConclusionsWe have clearly demonstrated that AHLOS varies between different hospitals, and that the most important patient-level explanatory variables are discharge mRS, dementia and inpatient complications. We highlight the potential importance of stroke volume in influencing these differences but cannot discount the potential effect of unmeasured confounders.

On the Definition of Latent-State-Trait Models With Autoregressive Effects

2017 ◽  
Vol 33 (4) ◽  
pp. 285-295 ◽  
Author(s):  
Michael Eid ◽  
Jana Holtmann ◽  
Philip Santangelo ◽  
Ulrich Ebner-Priemer
Keyword(s):  
Latent Variables ◽  
Time Lags ◽  
Situational Influences ◽  
Situational Effects ◽  
Classical Models ◽  
Carry Over ◽  
Definition Of ◽  
Time Specific ◽  
Open Question ◽  
Short Time

Abstract. In longitudinal studies with short time lags, classical models of latent state-trait (LST) theory that assume no carry-over effects between neighboring occasions of measurement are often inappropriate, and have to be extended by including autoregressive effects. The way in which autoregressive effects should be defined in LST models is still an open question. In a recently published revision of LST theory (LST-R theory), Steyer, Mayer, Geiser, and Cole (2015) stated that the trait-state-occasion (TSO) model ( Cole, Martin, & Steiger, 2005 ), one of the most widely applied LST models with autoregressive effects, is not an LST-R model, implying that proponents of LST-R theory might recommend not to apply the TSO model. In the present article, we show that a version of the TSO model can be defined on the basis of LST-R theory and that some of its restrictions can be reasonably relaxed. Our model is based on the idea that situational effects can change time-specific dispositions, and it makes full use of the basic idea of LST-R theory that dispositions to react to situational influences are dynamic and malleable. The latent variables of the model have a clear meaning that is explained in detail.

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