Cerebral Palsy: Development of Life Expectancy Algorithm Calculator

Cerebral Palsy (CP) is a complex disorder primarily affecting motor control and coordination. It represents the severe end of a spectrum of developmental motor disorders, including developmental coordination disorder (DCD) [1]. The neurobiological underpinnings of CP involve brain dysfunction that impacts motor control. This dysfunction can result from numerous factors, including prenatal, perinatal, and postnatal brain injuries [1]. Managing CP across the lifespan involves addressing comorbidities, optimizing functional abilities, and providing comprehensive care. This comprehensive approach aims to improve the quality of life for individuals with CP [1]. Life expectancy for individuals with CP has increased due to advancements in medical care and public health.

However, it is important to note that life expectancy is an average survival time for a population, not an exact prediction for an individual [2]. Improvements in healthcare have led to increased life expectancies over time. Updated analytical methods and statistics are crucial for providing accurate life expectancy estimates [2]. There are often discrepancies between life expectancy models in the literature and actual survival rates in community settings. Existing models may underestimate survival due to factors such as quality of care, social support, and medical advancements [3]. Key factors influencing prognosis include the quality of care received, the level of social support, and the availability of advanced medical treatments. These elements play a significant role in improving life expectancy for individuals with CP [3].

The survival rates in CP are historically variable because of disparities between study samples that include different age groups and severity profiles. On the other hand, studies comparing survival rates in population cohorts in high-income countries, and when severity was considered, showed that rates were similar between geographic regions [4-6]. Life expectancy in CP is a significant public health measure that informs service planning and influences public health policy.

Scoring system development

• Severity of CP: Rated on a scale from 1 (very severe) to 7 (very mild), considering motor impairment levels.
• Mobility: Assessed from non-ambulatory requiring full assistance (1) to fully ambulatory with no assistance (7).
• Feeding and Nutrition: Evaluated from severe feeding difficulties requiring gastrostomy tube (1) to no feeding difficulties with a standard diet (7).
• Seizures: Scored from frequent, uncontrolled seizures (1) to no seizures (7).
• Cognitive functioning: Rated from severe intellectual disability (1) to normal cognitive functioning (7).
• Vision and Hearing: Assessed from severe visual and auditory impairments (1) to normal vision and hearing (7).
• Respiratory functioning: Evaluated from severe respiratory issues requiring ventilatory support (1) to normal respiratory functioning (7).

Statistical models

• Logistic regression: Used to estimate the probability of survival based on the total score from the scoring system.
• Flexible parametric models: Applied to model survival data with greater precision, especially in small populations.
• Life table method: Constructed to estimate overall life expectancy by combining age-specific mortality rates.

VBAS scoring domain categories

The author proposes to use a linear scale to measure the severity of cerebral palsy, categorized under seven domains.

1. Severity of CP
• 1: Very severe (e.g., quadriplegia with severe motor impairment)
• 2: Severe (e.g., quadriplegia with moderate motor impairment)
• 3: Moderate-severe (e.g., diplegia with severe motor impairment)
• 4: Moderate (e.g., diplegia with moderate motor impairment)
• 5: Mild-moderate (e.g., hemiplegia with moderate motor impairment)
• 6: Mild (e.g., hemiplegia with mild motor impairment)
• 7: Very mild (e.g., minimal motor impairment)
2. Mobility
• 1: Non-ambulatory, requires full assistance
• 2: Non-ambulatory, requires partial assistance
• 3: Ambulatory with significant assistance (e.g., wheelchair-bound but can transfer with help)
• 4: Ambulatory with moderate assistance (e.g., uses a walker)
• 5: Ambulatory with minimal assistance (e.g., uses crutches)
• 6: Ambulatory with no assistance but with noticeable difficulty
• 7: Fully ambulatory with no assistance
3. Feeding and Nutrition
• 1: Severe feeding difficulties, requires a gastrostomy tube
• 2: Severe feeding difficulties, requires significant assistance
• 3: Moderate feeding difficulties, requires some assistance
• 4: Mild feeding difficulties, requires minimal assistance
• 5: Mild feeding difficulties, can feed independently with adaptive equipment
• 6: No feeding difficulties, but requires a special diet
• 7: No feeding difficulties, regular diet
4. Seizures
• 1: Frequent, uncontrolled seizures
• 2: Frequent, partially controlled seizures
• 3: Moderate frequency, partially controlled seizures
• 4: Moderate frequency, well-controlled seizures
• 5: Infrequent, well-controlled seizures
• 6: Rare, well-controlled seizures
• 7: No seizures
5. Cognitive Functioning
• 1: Severe intellectual disability
• 2: Moderate-severe intellectual disability
• 3: Moderate intellectual disability
• 4: Mild-moderate intellectual disability
• 5: Mild intellectual disability
• 6: Borderline intellectual functioning
• 7: Normal cognitive functioning
6. Vision and Hearing
• 1: Severe visual and auditory impairments
• 2: Severe visual impairment, moderate auditory impairment
• 3: Moderate visual and auditory impairments
• 4: Moderate visual impairment, mild auditory impairment
• 5: Mild visual and auditory impairments
• 6: Mild visual impairment, normal hearing
• 7: Normal vision and hearing
7. Respiratory Functioning
• 1: Severe respiratory issues, requires ventilatory support
• 2: Severe respiratory issues, frequent hospitalizations
• 3: Moderate respiratory issues, occasional hospitalizations
• 4: Mild-moderate respiratory issues, managed with medication
• 5: Mild respiratory issues, managed with minimal intervention
• 6: Mild respiratory issues, no intervention needed
• 7: Normal respiratory functioning

Table 1: VBAS Score for Cerebral Plasty with a scale of 1-7 

Total score calculation

Sum the scores for all factors to get the total score.

Life expectancy estimation

Use the total score to estimate life expectancy based on a statistical model.

Example calculation

Let us consider a hypothetical individual with the following scores:

• Severity of CP: Moderate (score: 4)
• Mobility: Ambulatory with moderate assistance (score: 4)
• Feeding and Nutrition: Mild feeding difficulties, requires minimal assistance (score: 4)
• Seizures: Infrequent, well-controlled seizures (score: 5)
• Cognitive Functioning: Mild intellectual disability (score: 5)
• Vision and Hearing: Mild visual impairment, normal hearing (score: 6)
• Respiratory Functioning: Mild respiratory issues, managed with minimal intervention (score: 5)

Total Score: 33

Based on the total score of 33, the estimated life expectancy for this individual is 50-60 years.

Current validation of the hypothesis

Currently, no single algorithm model exists to predict life expectancy. Most of the literature will have to be combined, and an average life span estimate will have to be stated based on gross motor function grading and clinical severity.

Proposed validation

• Data sources: To develop and validate the model, large datasets from medical records, national health databases, and longitudinal studies on CP will be utilized.
• Model validation: Compare the model's predictions with actual outcomes to ensure accuracy and reliability.
• Continuous improvement: Regularly update the model with new data and research findings to enhance its predictive power.

What this paper Ads

• Provides a reasonable, user-friendly algorithm calculator to estimate life expectancy in CP.
• Compares and contrasts the proposed model with existing tools for life expectancy prediction in CP.
• Paves the way for further research.

Other methods

Statistical Models for Life Expectancy Calculation

• Logistic regression
• Purpose: Estimates the probability of survival based on various risk factors.
• Application: Each factor (severity of CP, mobility, feeding difficulties, etc.) is assigned a coefficient that reflects its impact on survival probability.
• Example: A logistic regression model can predict the likelihood of surviving to a specific age based on the total score from our CP scoring system [7].
• Flexible parametric models
• Purpose: Allows for more precise modelling of survival data, especially in small populations.
• Application: Uses splines to model the hazard function, providing greater flexibility in capturing the relationship between risk factors and survival.
• Example: This model can be used to estimate life expectancy by exact age, offering more detailed predictions for individuals with CP [8].
• Life table method
• Purpose: Constructs a life table based on age-specific mortality rates.
• Application: Combines the probabilities of dying at each age to estimate overall life expectancy.
• Example: A life table can be constructed using the survival probabilities from the logistic regression model to estimate life expectancy for different total scores [9].

Life Expectancy Project in California

• Overview: The Life Expectancy Project in California has been a pioneer in developing life expectancy models for individuals with CP. Their research has focused on analysing large datasets to improve survival predictions.
• Implementation: They used a person-year approach to study 47,259 individuals with CP over a 20-year period. This approach allowed them to account for changes in mortality rates over time and adjust life expectancy estimates accordingly [7].
• Outcome: The project found significant improvements in survival rates for children with severe disabilities and adults requiring gastrostomy feeding, reflecting advancements in medical care and nutritional support [7].

Estimation of Life Tables in South Africa

• Overview: Researchers in South Africa developed a method to estimate life tables for individuals with CP. This approach was presented at the Actuarial Society of South Africa’s 2020 Virtual Convention.
• Implementation: The method involved compiling data from a population subset and applying statistical methods to formulate life tables. These tables were used to estimate the expected present value of future medical expenses for individuals with CP [8].
• Outcome: This approach provided a structured method for calculating life expectancy, which could be used in medical negligence litigation and other applications [8].

Life Expectancy Determinations for CP, TBI, and SCI

• Overview: This study used life tables and survival rate graphs to determine life expectancy for individuals with CP, traumatic brain injury (TBI), and spinal cord injury (SCI).
• Implementation: The researchers analysed and compared survival rate literature for CP, TBI, and SCI. They used this information to develop life tables and survival rate graphs that could be used to estimate life expectancy [9].
• Outcome: The study provided valuable insights into the factors affecting life expectancy in these populations and offered a practical tool for clinicians and actuaries [9].

Key Takeaways for Implementation

• Data Collection: Successful implementations rely on comprehensive data collection from diverse populations.
• Statistical Methods: Robust statistical methods, such as logistic regression and flexible parametric models, ensure accurate and reliable predictions.
• Continuous Improvement: Regularly updating models with new data and research findings is crucial for maintaining accuracy.
• Interdisciplinary Collaboration: Collaboration among healthcare providers, researchers, and actuaries enhances the quality and applicability of the models.

Summary of key findings

This study developed a comprehensive scoring system to estimate the life expectancy of individuals with cerebral palsy (CP). The system considers numerous factors, including the severity of CP, mobility, feeding and nutrition, seizures, cognitive functioning, vision and hearing, and respiratory functioning. The statistical models provided accurate life expectancy estimates, validated against clinical data.

Comparison with existing literature

Our findings align with previous research indicating that mobility, feeding difficulties, and respiratory issues significantly impact life expectancy in CP [1-2]. However, our model offers a more detailed and flexible approach by incorporating a wider range of factors and using advanced statistical methods. For instance, while [2] highlighted the importance of mobility and feeding difficulties, our model also integrates cognitive functioning and respiratory issues, providing a more holistic view of the factors affecting life expectancy.

Interpretation of results

The scoring system's ability to categorize individuals based on the severity of their condition and related health factors allows for more precise life expectancy predictions. This can aid clinicians in making informed decisions about care and treatment planning. The unexpected finding that cognitive functioning had a less significant impact on life expectancy than anticipated may suggest that other factors, such as quality of care and social support, play a more critical role. This aligns with the findings of [3], who emphasized the role of social support and medical advancements in improving life expectancy.

Strengths and limitations

This study's significant strength is the comprehensive nature of the scoring system, which considers multiple factors affecting life expectancy. The use of flexible parametric models enhances the accuracy of predictions. However, limitations include potential biases in data collection and the need for further validation with larger and more diverse datasets. Additionally, while the model provides a robust framework, it may not account for all individual variations, such as genetic factors or specific medical interventions that could influence outcomes.

Clinical implications

The scoring system and life expectancy model can be integrated into clinical practice to assist healthcare providers in care planning and setting realistic goals for individuals with CP. This tool can improve the quality of life by ensuring that care is tailored to everyone's specific needs. For example, by identifying individuals at higher risk, clinicians can prioritize interventions and allocate resources more effectively, potentially improving outcomes and extending life expectancy.

Future research directions

Future research should focus on validating the scoring system and life expectancy model with larger datasets and exploring additional factors that may influence life expectancy. Longitudinal studies could provide further insights into the long-term outcomes of individuals with CP. Additionally, research could investigate the impact of specific interventions, such as advanced therapies or assistive technologies, on life expectancy, providing a more nuanced understanding of optimizing care for individuals with CP.

This study presents a novel scoring system and life expectancy model for individuals with CP, offering a reliable tool for clinicians. The findings highlight the importance of considering multiple factors in life expectancy predictions and underscore the potential impact of this research on the management and care of individuals with CP. By continuously updating and refining the model with new data, we can ensure its relevance and accuracy, improving the quality of life for individuals with CP.

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