EconPapers    
Economics at your fingertips  
 

A mathematical model for predicting length of postoperative intensive care requirement following cardiac surgery in an Indian hospital

Goutam Dutta (), Ajay Naik (), Dipa Gosai () and Priyanko Ghosh ()
Additional contact information
Goutam Dutta: Indian Institute of Management
Ajay Naik: CIMS Hospital
Dipa Gosai: Shri K. K. Shastri Government Commerce College
Priyanko Ghosh: Indian Institute of Management

OPSEARCH, 2021, vol. 58, issue 2, No 3, 330-350

Abstract: Abstract Intensive care unit (ICU) is a critical resource in a hospital, especially in developing countries such as India. The length of ICU stay after a cardiac surgery is an important variable for effective use of this critical resource. In this context, a predictive model can help a hospital to make optimum use of its ICU occupancy. A study was thus conducted on ICU patients and data gather over a 1-year period in a hospital in India. The critical factors for prolonged ICU stay (more than 72 h) were identified using univariate and multivariate logistic regression and a predictive index was built based on model development set. The predictive index was tested on a validation set and the mean length of ICU stay appeared to increase with an increase in the risk score. In addition, the risk score was tested in case of mortality. Efficient use of the ICU facility is possible with the help of this predictive index.

Keywords: Health services; Medicine; Statistics; Forecasting; Regression; Developing countries (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12597-020-00480-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:opsear:v:58:y:2021:i:2:d:10.1007_s12597-020-00480-7

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-020-00480-7

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:opsear:v:58:y:2021:i:2:d:10.1007_s12597-020-00480-7