Cerebrovascular accidents associated with hip fractures: morbidity and mortality-5-year survival

Ran Atzmon1, Zachary T Sharfman2, Noa Efrati3

  • 1Department of Orthopaedic Surgery, Assuta Medical Center, Affiliated with the Faculty of Health and Science and Ben Gurion University, Ashdod, Israel.

Insights

Hip fractures increase cerebrovascular accident (CVA) risk, especially between years one and five post-surgery. Hypertension, atrial fibrillation, and diabetes are key risk factors, negatively impacting survival.

Area of Science:

  • Geriatric Medicine
  • Neurosurgery
  • Cardiology

Background:

  • Hip fractures are linked to a higher incidence of cerebrovascular accidents (CVAs) within the first year after surgery.
  • Limited long-term data exists on CVA occurrence and mortality following hip fracture.
  • This study aimed to identify risk factors for CVA and track mortality in hip fracture patients with over two years of follow-up.

Purpose of the Study:

  • To identify risk factors associated with cerebrovascular accidents (CVAs) after hip fracture.
  • To evaluate long-term mortality rates in patients who have experienced a hip fracture.
  • To establish a comprehensive understanding of post-hip fracture CVA and survival outcomes.

Main Methods:

  • A retrospective analysis comparing the past medical history of hip fracture patients with long-term survival and CVA occurrence.
  • Data on medical history, surgical interventions, CVA events, and mortality were extracted from an electronic medical record system.
  • Statistical significance was determined using a p-value less than 0.05 and a 95% confidence interval.

Main Results:

  • The study included 2,195 patients with a mean follow-up of 5 years.
  • 5.01% of patients (110) were diagnosed with post-fracture CVA, with 41 occurring in the first year and 55 between years 1-5.
  • Hypertension (HTN), atrial fibrillation (AF), and diabetes mellitus (DM) were significant risk factors for CVA (ORs ranging from 1.66 to 1.885, p<0.05).
  • Median survival was shorter for patients with CVA (51.12 months) compared to those without (59.60 months, p=0.033).

Conclusions:

  • Hypertension, atrial fibrillation, and diabetes mellitus are identified as significant risk factors for CVA post-hip fracture.
  • A substantial number of CVAs occur between the first and fifth year after hip fracture surgery.
  • CVA occurrence is a negative prognostic indicator for patient survival following hip fracture.
Abstract

Related Concept Videos

5-Number Summary01:04

5-Number Summary

In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
5.7K
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
723
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
433
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
811
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...
609
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
625