A Study of Factors Affecting the Length of Hospital Stay of COVID-19 Patients by Cox-Proportional Hazard Model in a South Indian Tertiary Care Hospital

Abstract

Background: The objective of the study was to identify the factors that alter the length of hospital stay of COVID-19 patients so we have an estimate of the duration of hospitalization of patients. To achieve this, we used a time to event analysis to arrive at factors that could alter the length of hospital stay, aiding in planning additional beds for any future rise in cases. Methods: Information about COVID-19 patients was collected between June and August 2020. The response variable was the time from admission to discharge of patients. Cox proportional hazard model was used to identify the factors that were associated with the length of hospital stay. Results: A total of 730 COVID-19 patients were included, of which 675 (92.5%) recovered and 55 (7.5%) were considered to be right-censored, that is, the patient died or was discharged against medical advice. The median length of hospital stay of COVID-19 patients who were hospitalized was found to be 7 days by the Kaplan Meier curve. The covariates that prolonged the length of hospital stay were found to be abnormalities in oxygen saturation (HR = 0.446, P < .001), neutrophil-lymphocyte ratio (HR = 0.742, P = .003), levels of D-dimer (HR = 0.60, P = .002), lactate dehydrogenase (HR = 0.717, P = .002), and ferritin (HR = 0.763, P = .037). Also, patients who had more than 2 chronic diseases had a significantly longer length of stay (HR = 0.586, P = .008) compared to those with no comorbidities. Conclusion: Factors that are associated with prolonged length of hospital stay of patients need to be considered in planning bed strength on a contingency basis.

Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model

“…The most commonly used statistical tool for analyzing time to event data is the Cox proportional hazards model. 7 An AFT model is a 9 This is in agreement with the results of our study, which showed that the AFT with log-logistic distribution was the most suitable model. This model showed that abnormal values of SpO 2 , creatinine, LDH, NLR, D-dimer, ferritin, TC, age >80 years, and coronary artery disease were associated with longer hospital stays.…”

“…The most commonly used statistical tool for analyzing time to event data is the Cox proportional hazards model. 7 An AFT model is a parametric survival model used to assess the effects of covariates on a response variable, and is an alternative to the Cox model for time-to-event data. AFT models do not require Cox proportional hazards modeling as an a priori test.…”

A Hospital based cross sectional study to find out factors associated with disease severity and length of hospital stay in COVID-19 patients in Tertiary Care Hospital of Ahmedabad city

“…In the current study, total 84% were recovered from illness while in similar other study out of 730 COVID-19 patients, 92.5% recovered and 7.5% were considered to be patient died or was discharged against medical advice (DAMA). The median length of hospital stay of COVID-19 patients was found to be 7 days (10) while in the current study median length of hospital stay of COVID-19 patients was 6 days.…”

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

“…This method minimizes the combination of squares error and weights and seeks the right combination that leads to a network with high generalizability (55,56) In the bibliography, many studies have been undertaken to identify the key risk factors for hospital LOS (8, 13-16, 61, 62). The top clinical variables affecting longer LOS in reviewed studies including: age (older age) (13,15,16,62) , comorbidities (8,14,61,62) (cardiovascular, hypertension, diabetes, respiratory diseases such asthma or COPD), loss of consciousness (8,14,62) , increased BUN (8,(14)(15)(16) , leukocytosis (8,61) , decreased SPO2 (low oxygen saturation) (13,15,16,61) , mechanical ventilation (oxygen therapy) (8,14,62) , pleural effusion (13,15,61) , dry cough (13,62) , and fever (8,15,16,62) . In the present study, the most important variables (n=20 predictors) were identi ed through correlation coe cient at the level of P-value <0.2 (feature selection).…”

“…As the pandemic worsens, identifying the resulting needs of patients and service providers has become a vital prerequisite. It needs anticipation of how long each case will need inpatient services (13,14). An exact approximation of the patients' LOS would be of substantial worth for scienti cally dealing with both medical resources and the distribution of caregivers (8,15).…”