## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  fig.width = 8,
  fig.height = 6
)

## ----setup--------------------------------------------------------------------
library(RiskScorescvd)
library(ggplot2)

## ----sample-data--------------------------------------------------------------
# Create sample patient data
sample_patients <- data.frame(
  patient_id = 1:6,
  Age = c(45, 55, 65, 70, 50, 60),
  Gender = c("male", "female", "male", "female", "male", "female"),
  diabetes = c(0, 1, 1, 0, 1, 0),
  smoker = c(1, 0, 1, 1, 0, 1),
  hypertension = c(1, 1, 0, 1, 1, 0),
  hyperlipidaemia = c(1, 0, 1, 1, 0, 1),
  family_history = c(1, 1, 0, 1, 1, 0),
  presentation_hstni = c(25, 50, 80, 30, 45, 35),
  systolic_bp = c(140, 130, 160, 135, 145, 125),
  heart_rate = c(75, 85, 95, 70, 80, 90),
  creatinine = c(1.0, 1.2, 1.5, 0.9, 1.1, 1.0),
  total_chol = c(200, 220, 180, 240, 210, 190),
  hdl_chol = c(45, 55, 40, 60, 50, 65)
)

print(sample_patients)

## ----heart-examples-----------------------------------------------------------
cat("HEART Score Calculations:\n")
cat("========================\n\n")

for(i in 1:3) {
  patient <- sample_patients[i, ]
  cat("Patient", i, "- Age:", patient$Age, ", Gender:", patient$Gender, "\n")
  
  # Manual HEART score calculation for demonstration
  history_score <- 1  # Moderately suspicious
  ecg_score <- 1      # Non-specific changes
  age_score <- ifelse(patient$Age < 45, 0, ifelse(patient$Age <= 64, 1, 2))
  risk_factors <- patient$diabetes + patient$smoker + patient$hypertension + patient$hyperlipidaemia + patient$family_history
  risk_score <- ifelse(risk_factors == 0, 0, ifelse(risk_factors <= 2, 1, 2))
  troponin_score <- ifelse(patient$presentation_hstni < 14, 0, ifelse(patient$presentation_hstni < 50, 1, 2))
  
  total_score <- history_score + ecg_score + age_score + risk_score + troponin_score
  risk_level <- ifelse(total_score <= 3, "Low", ifelse(total_score <= 6, "Moderate", "High"))
  
  cat("  - HEART Score:", total_score, "(", risk_level, "risk )\n")
  cat("  - Components: History=", history_score, ", ECG=", ecg_score, ", Age=", age_score, 
      ", Risk factors=", risk_score, ", Troponin=", troponin_score, "\n\n")
}

## ----edacs-examples-----------------------------------------------------------
cat("EDACS Score Calculations:\n")
cat("========================\n\n")

for(i in 1:3) {
  patient <- sample_patients[i, ]
  cat("Patient", i, "- Age:", patient$Age, ", Gender:", patient$Gender, "\n")
  
  # Manual EDACS calculation
  age_score <- ifelse(patient$Age <= 50, 2, 4)
  sex_score <- ifelse(patient$Gender == "male", 6, 0)
  known_cad <- 0  # Assume no known CAD for example
  pain_radiation <- 1  # Assume pain radiates
  
  # Simplified EDACS calculation
  edacs_score <- age_score + sex_score + known_cad + pain_radiation
  risk_level <- ifelse(edacs_score < 16, "Low risk", "Not low risk")
  
  cat("  - EDACS Score:", edacs_score, "(", risk_level, ")\n")
  cat("  - Components: Age=", age_score, ", Sex=", sex_score, ", Known CAD=", known_cad, 
      ", Pain radiation=", pain_radiation, "\n\n")
}

## ----grace-examples-----------------------------------------------------------
cat("GRACE Score Calculations:\n")
cat("========================\n\n")

for(i in 1:3) {
  patient <- sample_patients[i, ]
  cat("Patient", i, "- Age:", patient$Age, ", Gender:", patient$Gender, "\n")
  
  # Simplified GRACE calculation (approximate)
  age_points <- patient$Age * 0.7  # Simplified age scoring
  hr_points <- patient$heart_rate * 0.4  # Simplified HR scoring
  sbp_points <- max(0, (200 - patient$systolic_bp) * 0.3)  # Simplified SBP scoring
  creat_points <- patient$creatinine * 15  # Simplified creatinine scoring
  
  grace_score <- round(age_points + hr_points + sbp_points + creat_points)
  risk_level <- ifelse(grace_score <= 88, "Low", ifelse(grace_score <= 110, "Intermediate", "High"))
  
  cat("  - GRACE Score:", grace_score, "(", risk_level, "risk )\n")
  cat("  - Estimated mortality risk at 6 months\n\n")
}

## ----timi-examples------------------------------------------------------------
cat("TIMI Score Calculations:\n")
cat("=======================\n\n")

for(i in 1:3) {
  patient <- sample_patients[i, ]
  cat("Patient", i, "- Age:", patient$Age, ", Gender:", patient$Gender, "\n")
  
  # TIMI risk factors (1 point each)
  age_65 <- ifelse(patient$Age >= 65, 1, 0)
  risk_factors_3 <- ifelse((patient$diabetes + patient$smoker + patient$hypertension + 
                           patient$hyperlipidaemia + patient$family_history) >= 3, 1, 0)
  known_cad <- 0  # Assume no known CAD
  aspirin_use <- 1  # Assume recent aspirin use
  severe_angina <- 1  # Assume severe symptoms
  elevated_markers <- ifelse(patient$presentation_hstni > 14, 1, 0)
  st_deviation <- 1  # Assume ST changes
  
  timi_score <- age_65 + risk_factors_3 + known_cad + aspirin_use + severe_angina + elevated_markers + st_deviation
  risk_level <- ifelse(timi_score == 0, "Very Low", 
                      ifelse(timi_score <= 2, "Low", 
                            ifelse(timi_score <= 4, "Intermediate", "High")))
  
  cat("  - TIMI Score:", timi_score, "(", risk_level, "risk )\n")
  cat("  - 14-day risk of death/MI/urgent revascularization\n\n")
}

## ----ascvd-examples-----------------------------------------------------------
cat("ASCVD 10-Year Risk Calculations:\n")
cat("===============================\n\n")

for(i in 1:3) {
  patient <- sample_patients[i, ]
  cat("Patient", i, "- Age:", patient$Age, ", Gender:", patient$Gender, "\n")
  
  # Simplified ASCVD risk estimation (this is a rough approximation)
  base_risk <- ifelse(patient$Gender == "male", 0.12, 0.08)
  age_factor <- (patient$Age - 40) * 0.01
  chol_factor <- (patient$total_chol - 200) * 0.0001
  hdl_factor <- (50 - patient$hdl_chol) * 0.0005
  bp_factor <- (patient$systolic_bp - 120) * 0.0003
  diabetes_factor <- patient$diabetes * 0.02
  smoker_factor <- patient$smoker * 0.03
  
  risk_10yr <- (base_risk + age_factor + chol_factor + hdl_factor + bp_factor + 
                diabetes_factor + smoker_factor) * 100
  risk_10yr <- max(1, min(40, risk_10yr))  # Cap between 1-40%
  
  risk_category <- ifelse(risk_10yr < 5, "Low", 
                         ifelse(risk_10yr < 7.5, "Borderline",
                               ifelse(risk_10yr < 20, "Intermediate", "High")))
  
  cat("  - 10-year ASCVD Risk:", round(risk_10yr, 1), "% (", risk_category, "risk )\n")
  cat("  - Total Chol:", patient$total_chol, "mg/dL, HDL:", patient$hdl_chol, "mg/dL\n\n")
}

## ----distributions, fig.height=10, fig.width=12-------------------------------
# Set seed for reproducible results
set.seed(123)
n <- 1000

# Generate theoretical score distributions
heart_scores <- sample(0:10, n, replace = TRUE, 
                      prob = c(0.05, 0.08, 0.12, 0.18, 0.20, 0.15, 0.10, 0.06, 0.04, 0.02, 0.01))
heart_risk <- cut(heart_scores, breaks = c(-1, 3, 6, 10), labels = c("Low", "Moderate", "High"))

edacs_scores <- pmax(0, rnorm(n, mean = 18, sd = 8))
edacs_risk <- ifelse(edacs_scores < 16, "Low risk", "Not low risk")

grace_scores <- pmax(50, pmin(200, rnorm(n, mean = 95, sd = 25)))
grace_risk <- cut(grace_scores, breaks = c(0, 88, 110, 200), labels = c("Low", "Intermediate", "High"))

timi_scores <- sample(0:7, n, replace = TRUE, 
                     prob = c(0.20, 0.25, 0.20, 0.15, 0.10, 0.06, 0.03, 0.01))
timi_risk <- cut(timi_scores, breaks = c(-1, 0, 2, 4, 7), 
                labels = c("Very Low", "Low", "Intermediate", "High"))

ascvd_risk <- pmax(1, pmin(40, rgamma(n, shape = 2, rate = 0.3)))
ascvd_category <- cut(ascvd_risk, breaks = c(0, 5, 7.5, 20, 100), 
                     labels = c("Low", "Borderline", "Intermediate", "High"))

# Create individual plots
p1 <- ggplot(data.frame(score = heart_scores, risk = heart_risk), aes(x = score, fill = risk)) +
  geom_histogram(bins = 11, alpha = 0.8, position = "dodge") +
  scale_fill_manual(values = c("Low" = "#2E8B57", "Moderate" = "#FF8C00", "High" = "#DC143C")) +
  labs(title = "HEART Score Distribution", x = "HEART Score (0-10)", y = "Count", fill = "Risk Level") +
  theme_minimal() + theme(legend.position = "bottom")

p2 <- ggplot(data.frame(score = edacs_scores, risk = edacs_risk), aes(x = score, fill = risk)) +
  geom_histogram(bins = 30, alpha = 0.8) +
  geom_vline(xintercept = 16, color = "red", linetype = "dashed", size = 1) +
  scale_fill_manual(values = c("Low risk" = "#2E8B57", "Not low risk" = "#DC143C")) +
  labs(title = "EDACS Score Distribution", x = "EDACS Score", y = "Count", fill = "Risk Level") +
  theme_minimal() + theme(legend.position = "bottom") +
  xlim(0, 50)

p3 <- ggplot(data.frame(score = grace_scores, risk = grace_risk), aes(x = score, fill = risk)) +
  geom_histogram(bins = 30, alpha = 0.8) +
  scale_fill_manual(values = c("Low" = "#2E8B57", "Intermediate" = "#FF8C00", "High" = "#DC143C")) +
  labs(title = "GRACE Score Distribution", x = "GRACE Score", y = "Count", fill = "Risk Level") +
  theme_minimal() + theme(legend.position = "bottom")

p4 <- ggplot(data.frame(score = timi_scores, risk = timi_risk), aes(x = score, fill = risk)) +
  geom_histogram(bins = 8, alpha = 0.8) +
  scale_fill_manual(values = c("Very Low" = "#90EE90", "Low" = "#2E8B57", "Intermediate" = "#FF8C00", "High" = "#DC143C")) +
  labs(title = "TIMI Score Distribution", x = "TIMI Score (0-7)", y = "Count", fill = "Risk Level") +
  theme_minimal() + theme(legend.position = "bottom")

p5 <- ggplot(data.frame(risk = ascvd_risk, category = ascvd_category), aes(x = risk, fill = category)) +
  geom_histogram(bins = 30, alpha = 0.8) +
  scale_fill_manual(values = c("Low" = "#2E8B57", "Borderline" = "#FFD700", "Intermediate" = "#FF8C00", "High" = "#DC143C")) +
  labs(title = "ASCVD 10-Year Risk Distribution", x = "10-Year Risk (%)", y = "Count", fill = "Risk Category") +
  theme_minimal() + theme(legend.position = "bottom")

# Display plots in a grid
library(gridExtra)
grid.arrange(p1, p2, p3, p4, p5, ncol = 2, nrow = 3)

## ----summary-stats------------------------------------------------------------
cat("Summary Statistics for Risk Score Distributions:\n")
cat("==============================================\n\n")

cat("HEART Score:\n")
cat("  Mean:", round(mean(heart_scores), 2), "\n")
cat("  Risk Distribution:", table(heart_risk), "\n\n")

cat("EDACS Score:\n")
cat("  Mean:", round(mean(edacs_scores), 2), "\n")
cat("  Risk Distribution:", table(edacs_risk), "\n\n")

cat("GRACE Score:\n")
cat("  Mean:", round(mean(grace_scores), 2), "\n")
cat("  Risk Distribution:", table(grace_risk), "\n\n")

cat("TIMI Score:\n")
cat("  Mean:", round(mean(timi_scores), 2), "\n")
cat("  Risk Distribution:", table(timi_risk), "\n\n")

cat("ASCVD 10-Year Risk:\n")
cat("  Mean:", round(mean(ascvd_risk), 2), "%\n")
cat("  Risk Distribution:", table(ascvd_category), "\n")