Package {basil}

Type:	Package
Title:	Survival Prediction for Severe Limb Ischaemia (BASIL Model)
Version:	0.1.0
Description:	Predicts survival for patients with severe limb ischaemia using the prognostic model developed from the Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial (Bradbury and others (2010) <doi:10.1016/j.jvs.2010.01.077>). The model is an accelerated failure time Weibull regression. The package is intended for research and audit; it is not a substitute for clinical judgement and its predictions should not be used as the sole basis for clinical decisions.
License:	MIT + file LICENSE
Encoding:	UTF-8
URL:	https://github.com/chrislongros/basil
BugReports:	https://github.com/chrislongros/basil/issues
Suggests:	tinytest
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-07-07 14:34:36 UTC; chris
Author:	Christos Longros [aut, cre], Nikolaos Papatheodorou [aut]
Maintainer:	Christos Longros <chris.longros@gmail.com>
Repository:	CRAN
Date/Publication:	2026-07-16 13:00:12 UTC

BASIL predicted survival

Description

Predicts the probability of survival at one or more time points for a patient with severe limb ischaemia, using the BASIL trial survival prediction model (Bradbury and others, 2010). The model is an accelerated failure time Weibull regression; coefficients are those of the official BASIL prognostic spreadsheet.

Usage

basil(
  age,
  tissue_loss,
  bmi,
  creatinine,
  bollinger,
  smoking,
  mi_angina,
  stroke_tia,
  ankle_pressures_n,
  max_ankle_pressure,
  times = c(0.5, 1, 2),
  shrunk = TRUE
)

Arguments

Details

Two composite variables are entered as categories rather than raw values, because their derivations are not fully specified in the spreadsheet: creatinine as "low", "medium", "high" or "missing", and bollinger (the below-knee Bollinger angiogram score) as "<5", ">=5" or "unknown". Age is clamped to 40-95 and maximum ankle pressure to 0-200, as in the deployed model.

Value

A named numeric vector of survival probabilities at times, with the linear predictor attached as attribute "lp".

Examples

# The BASIL spreadsheet's worked example
basil(age = 80, tissue_loss = TRUE, bmi = "30+", creatinine = "medium",
      bollinger = "<5", smoking = "ex", mi_angina = TRUE, stroke_tia = TRUE,
      ankle_pressures_n = 0, max_ankle_pressure = 0)

Categorise a serum creatinine value for BASIL

Description

Maps a raw serum creatinine value to the category used by the creatinine argument of basil. The cut points are those of the BASIL model (Bradbury and others, 2010): below 88, 88-115, and above 115 \mumol/L for low, medium and high, respectively.

Usage

basil_creatinine_category(x, units = c("umol", "mgdl"))

Arguments

Value

A character vector of "low", "medium", "high" or "missing", suitable for the creatinine argument of basil.

Examples

basil_creatinine_category(c(70, 100, 130, NA))
basil_creatinine_category(1.5, units = "mgdl")  # 1.5 mg/dL -> 132.6 umol/L -> "high"

BASIL predicted survival for a cohort

Description

Applies basil to each row of a data frame, appending the linear predictor and the predicted survival at each of times as columns.

Usage

basil_risk(data, times = c(0.5, 1, 2), shrunk = TRUE)

Arguments

Value

data with an lp column and one surv_t<time> column for each entry in times.

Examples

patients <- data.frame(
    age = c(80, 65), tissue_loss = c(TRUE, FALSE),
    bmi = c("30+", "20-25"), creatinine = c("medium", "low"),
    bollinger = c("<5", ">=5"), smoking = c("ex", "non"),
    mi_angina = c(TRUE, FALSE), stroke_tia = c(TRUE, FALSE),
    ankle_pressures_n = c(0, 2), max_ankle_pressure = c(0, 80))
basil_risk(patients)