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Related Concept Videos

Actuarial Approach01:20

Actuarial Approach

78
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
78
Hazard Ratio01:12

Hazard Ratio

122
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
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Hazard Rate01:11

Hazard Rate

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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

138
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
138
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Relative Risk01:12

Relative Risk

170
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Updated: Jul 2, 2025

An R-Based Landscape Validation of a Competing Risk Model
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A Simple Risk Formula for the Prediction of COVID-19 Hospital Mortality.

Jiří Plášek1,2, Jozef Dodulík1, Petr Gai3

  • 1Department of Internal Medicine and Cardiology, University Hospital Ostrava, 708 52 Ostrava, Czech Republic.

Infectious Disease Reports
|February 23, 2024
PubMed
Summary

Age and disease severity are key predictors of hospital mortality in unvaccinated COVID-19 patients. Older age and severe respiratory symptoms significantly increase the risk of death in hospitalized individuals.

Keywords:
COVID-19SARS-CoV-2acute respiratory infectionmortalityprediction scorerisk rule

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Area of Science:

  • Infectious Diseases
  • Epidemiology
  • Critical Care Medicine

Background:

  • Hospitalized patients with SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection face substantial morbidity and mortality.
  • Understanding risk factors for hospital death is crucial for optimizing patient care, particularly in unvaccinated populations.
  • The 2021 spring wave in the Czech Republic provided a critical period to study outcomes in non-vaccinated individuals.

Purpose of the Study:

  • To identify and assess the primary risk factors associated with hospital mortality among non-vaccinated patients diagnosed with SARS-CoV-2 acute respiratory infection.
  • To quantify the impact of specific predictors, such as age and disease severity, on the likelihood of hospital death.

Main Methods:

  • Retrospective analysis of 790 non-vaccinated patients hospitalized with PCR-confirmed SARS-CoV-2 infection between January and March 2021.
  • Data collected from two university and five rural hospitals in the Czech Republic.
  • Multivariate regression analysis was employed to identify significant predictors of hospital mortality, focusing on age and oxygenation status.

Main Results:

  • A significant proportion of analyzed patients (35.7%) died during hospitalization.
  • Age and disease severity (based on oxygenation status) were identified as highly significant predictors of hospital mortality (p < 0.0001).
  • A 10-year increase in age was associated with a 2.5-fold increase in mortality risk; a unit increase in oxygenation status correlated with a 20-fold increase in mortality risk.

Conclusions:

  • Age and the severity of respiratory compromise are critical determinants of hospital mortality in unvaccinated COVID-19 patients.
  • Clinical assessment of oxygenation status and patient age are vital for predicting mortality risk and guiding clinical management decisions.
  • These findings underscore the importance of vaccination and prompt intervention for severe cases to mitigate mortality.