Key predictors of mortality in Crimean-Congo haemorrhagic fever: a retrospective multicentre cohort study

Deniz Güllü1, Defne Yigci2, Nurcan Baykam3

  • 1School of Medicine, Koç University, Istanbul, Türkiye; Graduate School of Health Sciences, Koç University, Istanbul, Türkiye; Koç University İşbank Center for Infectious Diseases (KUISCID), Koç University, Istanbul, Türkiye.

Insights

Age and diabetes are key predictors of mortality in Crimean-Congo Hemorrhagic Fever (CCHF). Early ribavirin treatment may improve survival rates for CCHF patients.

Area of Science:

  • Infectious Diseases
  • Epidemiology
  • Public Health

Background:

  • Crimean-Congo Hemorrhagic Fever (CCHF) is a tick-borne viral disease with significant mortality.
  • Identifying predictors of mortality is crucial for effective patient management and resource allocation.
  • Understanding demographic and clinical features of CCHF patients in Türkiye is essential for regional health strategies.

Purpose of the Study:

  • To identify key predictors of mortality in hospitalized Crimean-Congo Hemorrhagic Fever (CCHF) patients in Türkiye.
  • To characterize the demographic and clinical profile of CCHF patients.
  • To evaluate the impact of early ribavirin administration on CCHF patient outcomes.

Main Methods:

  • Retrospective study of 1103 laboratory-confirmed CCHF patients across 18 hospitals in Türkiye (2019-2024).
  • Data collected via an online system, including demographic, clinical, and treatment information.
  • Statistical analyses included univariate and time-dependent Cox regression to identify mortality predictors.

Main Results:

  • The study included 1103 CCHF patients; 5.1% (56/1103) mortality rate was observed.
  • Age ≥50 years (OR=3.1) and diabetes mellitus (OR=4.49) were significant predictors of increased mortality.
  • Early ribavirin administration (≤96 hours) was significantly associated with reduced mortality (aHR=0.21).

Conclusions:

  • Age and comorbidities like diabetes mellitus are significant predictors of mortality in CCHF patients.
  • Early identification of high-risk patients and timely intervention are critical for improving outcomes.
  • Early administration of ribavirin shows promise in reducing CCHF-related mortality.
Abstract

Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
675
Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
12.3K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
285
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
532
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
822
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
57