probability_of_survival: Calculate Probability of Survival Using TRISS Method

In traumar: Calculate Metrics for Trauma System Performance

Calculate Probability of Survival Using TRISS Method

Description

This function calculates the probability of survival (Ps) for trauma patients based on the Trauma and Injury Severity Score (TRISS) methodology. TRISS combines physiological and anatomical data to predict survival likelihood using a logistic regression model. The function incorporates trauma type, patient age, Revised Trauma Score (RTS), and Injury Severity Score (ISS) into the calculation. Probability of survival is expressed as a percentage.

Usage

probability_of_survival(trauma_type, age, rts, iss)

Arguments

trauma_type	Character vector indicating the type of trauma ("Blunt" or "Penetrating"). Different methods exist for calculating probability of survival for burn patients, and so these records are excluded here.
age	Numeric vector indicating the patient's age in years.
rts	Numeric vector indicating the patient's Revised Trauma Score (RTS).
iss	Numeric vector indicating the patient's Injury Severity Score (ISS).

Details

The methodology used in the calculation of survival probabilities aligns with the coefficients published in Norouzi et al. (2013) and Merchant et al. (2023). Consistent with Boyd et al. (1987), probability_of_survival() does not treat patients under 15 years of age differently and accounts for penetrating injuries similarly to other age groups. Norouzi et al. (2013) and Merchant et al. (2023) use the updated TRISS coefficients to calculate survival probabilities for penetrating traumas with the same coefficients as for blunt traumas. If this approach is preferred, please take note and adjust accordingly.

Value

Numeric vector of predicted probabilities of survival on a scale from 0 to 1.

Author(s)

Nicolas Foss, Ed.D., MS

References

Boyd CR, Tolson MA, Copes WS. (1987). Evaluating trauma care: the TRISS method. Trauma Score and the Injury Severity Score. J Trauma. 1987 Apr;27(4):370-8. PMID: 3106646.

Merchant AAH, Shaukat N, Ashraf N, Hassan S, Jarrar Z, Abbasi A, et al. (2023). Which curve is better? A comparative analysis of trauma scoring systems in a South Asian country. Trauma Surgery & Acute Care Open. 2023;8:e001171. doi:10.1136/tsaco-2023-001171

Norouzi V, Feizi I, Vatankhah S, Pourshaikhian M. (2013). Calculation of the probability of survival for trauma patients based on trauma score and the injury severity score model in fatemi hospital in ardabil. Arch Trauma Res. 2013 Spring;2(1):30-5. doi:10.5812/atr.9411. Epub 2013 Jun 1. PMID: 24396787; PMCID: PMC3876517.

Examples

# Example usage:
trauma_data <- data.frame(
  Trauma_Type = c("Blunt", "Penetrating"),
  Patient_Age_Years = c(30, 60),
  RTS = c(7.84, 6.90),
  ISS = c(10, 25)
)

# Run the function on example data
result <- trauma_data |>
  dplyr::mutate(Ps = probability_of_survival(
    trauma_type = Trauma_Type,
    age = Patient_Age_Years,
    rts = RTS,
    iss = ISS
  ))

# Print the result
result

traumar documentation built on Jan. 8, 2026, 9:10 a.m.