Recommendations on basic requirements for intensive care units: structural and organizational aspects

Valentin, Andreas; Ferdinande, Patrick

doi:10.1007/s00134-011-2300-7

Cited by 290 publications

(223 citation statements)

References 60 publications

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“…We point out here that, as reported in [41], an ICU should accommodate between 8 and 12 beds. From [11, Table 2], where authors apply structured and standard hidden Markov models for estimating the transmission rate of MRSA among patients within a hospital, we consider a general transmission rate for a well-mixed population of patients equal toβ = 0.329 days −1 .…”

Section: Numerical Resultsmentioning

confidence: 50%

“…(1), β =β/9 represents in our model the rate at which each susceptible patient is infected by each infected patient under homogeneous mixing assumptions. We introduce heterogeneity in our model in order to analyse two different control strategies commonly implemented to avoid outbreaks of MRSA: (i) a proper organisation of rooms, trying to configure the ICU in an uncrowded fashion when possible [41], and (ii) the isolation of the individual which is found to start an outbreak within the ICU [14]. Thus, when patient 1 initiates the outbreak (initial state x = (I, S, .…”

Section: Numerical Resultsmentioning

confidence: 99%

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Stochastic descriptors in an SIR epidemic model for heterogeneous individuals in small networks

López‐García

2016

Mathematical Biosciences

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We continue here the work initiated in [13], and analyse an SIR epidemic model for the spread of an epidemic among the members of a small population of N individuals, defined in terms of a continuous-time Markov chain X . We propose a structure by levels and sub-levels of the state space of the process X , and present two different orders, Orders A and B, for states within each sub-level, which are related to a matrix and a scalar formalism, respectively, when developing our analysis. Stochastic descriptors regarding the length and size of an outbreak, the maximum number of individuals simultaneously infected during an outbreak, the fate of a particular individual within the population, and the number of secondary cases caused by a certain individual until he recovers, are deeply analysed. Our approach is illustrated by carrying out a set of numerical results regarding the spread of the nosocomial pathogen Methicillin-resistant Staphylococcus Aureus among the patients within an intensive care unit. In this application, our interest is in analysing the effectiveness of control strategies (the isolation of the patient initiating the outbreak and the proper room configuration of the intensive care unit) that intrinsically introduce heterogeneities among the members of the population.Keywords Continuous-time Markov chain · Maximum number of infected individuals · Length and size of an outbreak · Individual fate · Nosocomial infections

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Section: Numerical Resultsmentioning

confidence: 50%

Section: Numerical Resultsmentioning

confidence: 99%

Stochastic descriptors in an SIR epidemic model for heterogeneous individuals in small networks

López‐García

2016

Mathematical Biosciences

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“…There is no substitute for an adequate number of critical care beds to which appropriate surgical patients can be admitted. 17,18,25 In our busy public sector hospitals with financial and organizational constraints, we suggest allocation of a dedicated isolated space in the vicinity of wards in the form 4-6 bedded High Dependency Unit can cover all essential aspect of management of critical surgical patients.…”

Section: Discussionmentioning

confidence: 99%

High Dependency Unit; Impact of Care on Outcome of Surgical Patients an Experience of Surgical Unit of District Teaching Hospital.

Jahan¹

2017

TPMJ

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ABSTRACT… Objectives: To study the impact of "High Dependency Unit" care on outcome of surgical patients. Study Design: Descriptive, Prospective. Setting: Surgical Department of Aziz Bhatti Shaheed (Teaching) Hospital. Nawaz Sharif medical College, University of the Gujrat. Study Period: January 1 st to December 31th 2016. Results: A total of 275 patients of all age groups from surgical and allied specialities requiring dedicated continues monitoring were admitted in High Dependency Unit (HDU) of the surgical department. Out of 275 patients 165 were admitted through emergency room after some surgical operation, whereas 115 patients are from those who were electively operated and shifted in HDU for management and monitoring due to some anaesthesia related complications or comorbidities. Conclusion: The High Dependency Unit care promotes efficient and effective care by increasing the flexibility of patient triage, utilizing resources efficiently, and providing cost effective timely care to serious surgical patient admitted in surgical wards. Key words:Intensive Care, Postop Care, Recovery, Critical Illness, HDU Mortality. Article Citation: Jahan S, Aziz S, Ateeq M, Asif M. High dependency unit; impact of care on outcome of surgical patients an experience of surgical unit of district teaching hospital

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“…21 A survey 17 of intensivists on patient to intensivist ratios indicated that the mean ratio of patient to attending physician was 13 to 1, with an upper limit of 24 to 1 at night. In that study, 17 factors that affected physician to patient ratios included number of ICU beds, mean daily census, and maximum service size (number of physicians).…”

Section: Discussionmentioning

confidence: 99%

Provider to Patient Ratios for Nurse Practitioners and Physician Assistants in Critical Care Units

Kleinpell¹,

Ward²,

Kelso³

et al. 2015

American Journal of Critical Care

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Recommendations on basic requirements for intensive care units: structural and organizational aspects

Abstract: Objective: To provide guidance and recommendations for the planning or renovation of intensive care units (ICUs) with respect to the specific characteristics relevant to organizational and structural aspects of intensive care medicine.

Cited by 290 publications

References 60 publications

Stochastic descriptors in an SIR epidemic model for heterogeneous individuals in small networks

Stochastic descriptors in an SIR epidemic model for heterogeneous individuals in small networks

High Dependency Unit; Impact of Care on Outcome of Surgical Patients an Experience of Surgical Unit of District Teaching Hospital.

Provider to Patient Ratios for Nurse Practitioners and Physician Assistants in Critical Care Units

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