ABSTRACT
Background: Epidemiological data on critically ill patients is crucial for understanding resource utilisation, gaps in quality of care and for supporting surveillance of endemic or emerging diseases. We report the epidemiology of critically ill patients from 17 intensive care units (ICUs) in Nepal using an established and standardised ICU registry. Methods: The ICU registry data is collected prospectively and includes data on case mix, severity, organ support and outcomes. We conducted a retrospective observational study with all adult (≥18 years) critically ill patients admitted to 17 ICUs in Nepal between September 2019 and September 2022. We report on case mix, treatment received, severity of illness, standardised mortality rates (SMR), discharge outcomes and ICU service activity. Descriptive statistics were used to report the findings. Results: Of the 18603 unique admissions, 14% were operative, with 35% emergency surgeries. Patients' median age was 57 (IQR 40-71) and 59% were male. Hypertension and diabetes were common comorbidities and pneumonia accounted for 26% of all admissions. During the ICU stay, 39% of patients received mechanical ventilation, 29% received vasoactive medication and 10% received renal replacement therapy. The median predicted risk of death was 0.1 (IQR 0.1-0.3) using APACHE II and 0.2 (IQR 0.1-0.4) using eTropICS. The median SMR was 0.7 (IQR 0.5-0.8) and 0.8 (IQR 0.6-1.4) using eTropICS and APACHE II, respectively. Median length of stay was 4 days (IQR 2-7). Eighteen percent died in the ICU;of those alive at discharge, 12% went home, 84% went to another department and 3% went to another hospital. COVID-19 was the most common notifiable disease reported (12% of all admissions). Median ICU turnover was 9% (IQR 6-14) with bed capacity ranging from 43-278. Conclusions: These findings should guide forecasting and service planning to ensure ICUs can optimally care for critically ill patients in Nepal.
ABSTRACT
INTRODUCTION: Coronavirus Disease 2019 is a primarily respiratory illness that can cause thrombotic disorders. Elevation of D-dimer is a potential biomarker for poor prognosis in COVID-19, though optimal cutoff value for D-dimer to predict mortality has not yet been established. This study aims to assess the accuracy of admission D-dimer in the prognosis of COVID-19 and to establish the optimal cutoff D-dimer value to predict hospital mortality. METHODS: Clinical and laboratory parameters and outcomes of confirmed COVID-19 cases admitted to four hospitals in Kathmandu were retrospectively analyzed. Admitted COVID-19 cases with recorded D-dimer and definitive outcomes were included consecutively. D-dimer was measured using immunofluorescence assay and reported in Fibrinogen Equivalent Unit (µg/ml). The receiver operating characteristic curve was used to determine the accuracy of D-dimer in predicting mortality, and to calculate the optimal cutoff value, based on which patients were divided into two groups and predictive value of D-dimer for mortality was measured. RESULTS: 182 patients were included in the study out of which 34(18.7%) died during the hospital stay. The mean admission D-dimer among surviving patients was 1.067 µg/ml (±1.705 µg/ml), whereas that among patients who died was 3.208 µg/ml (±2.613 µg/ml). ROC curve for D-dimer and mortality gave an area under the curve of 0.807 (95% CI 0.728-0.886, p<0.001). Optimal cutoff value for D-dimer was 1.5 µg/ml (sensitivity 70.6%, specificity 78.4%). On Cox proportional hazards regression analysis, the unadjusted hazard ratio for high D-dimer was 6.809 (95% CI 3.249-14.268, p<0.001), and 5.862 (95% CI 2.751-12.489, p<0.001) when adjusted for age. CONCLUSION: D-dimer value on admission is an accurate biomarker for predicting mortality in patients with COVID-19. 1.5 µg/ml is the optimal cutoff value of admission D-dimer for predicting mortality in COVID-19 patients.