Identification of predictors and model for predicting prolonged length of stay in dengue patients

Md. Ansari, Dinesh Jain (), Haripriya Harikumar, Santu Rana, Sunil Gupta, Sandeep Budhiraja and Svetha Venkatesh
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Md. Ansari: Max Super Specialty Hospital
Dinesh Jain: Max Super Specialty Hospital
Haripriya Harikumar: Deakin University
Santu Rana: Deakin University
Sunil Gupta: Deakin University
Sandeep Budhiraja: Max Super Specialty Hospital
Svetha Venkatesh: Deakin University

Health Care Management Science, 2021, vol. 24, issue 4, No 8, 786-798

Abstract: Abstract Purpose: Our objective is to identify the predictive factors and predict hospital length of stay (LOS) in dengue patients, for efficient utilization of hospital resources. Methods: We collected 1360 medical patient records of confirmed dengue infection from 2012 to 2017 at Max group of hospitals in India. We applied two different data mining algorithms, logistic regression (LR) with elastic-net, and random forest to extract predictive factors and predict the LOS. We used an area under the curve (AUC), sensitivity, and specificity to evaluate the performance of the classifiers. Results: The classifiers performed well, with logistic regression (LR) with elastic-net providing an AUC score of 0.75 and random forest providing a score of 0.72. Out of 1148 patients, 364 (32%) patients had prolonged length of stay (LOS) (> 5 days) and overall hospitalization mean was 4.03 ± 2.44 days (median ± IQR). The highest number of dengue cases belonged to the age group of 10-20 years (21.1%) with a male predominance. Moreover, the study showed that blood transfusion, emergency admission, assisted ventilation, low haemoglobin, high total leucocyte count (TLC), low or high haematocrit, and low lymphocytes have a significant correlation with prolonged LOS. Conclusion: Our findings demonstrated that the logistic regression with elastic-net was the best fit with an AUC of 0.75 and there is a significant association between LOS greater than five days and identified patient-specific variables. This method can identify the patients at highest risks and help focus time and resources.

Keywords: Patient’s length of stay (LOS); Dengue; Predictive models; Healthcare; Elastic-net; Random forest (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10729-021-09571-3

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