diff --git a/covid19_drdfm/streamlit/pages/2_Comparative_Run_Analysis.py b/covid19_drdfm/streamlit/pages/2_Comparative_Run_Analysis.py
index 96b9729..54e6a19 100644
--- a/covid19_drdfm/streamlit/pages/2_Comparative_Run_Analysis.py
+++ b/covid19_drdfm/streamlit/pages/2_Comparative_Run_Analysis.py
@@ -21,7 +21,9 @@ def center_title(text):
 center_title("Comparative Run Analysis")
 
 # Parameter to runs
-path_to_results = Path(st.text_input("Path directory of runs", value="./covid19_drdfm/data/example-data"))
+run_paths_string = "./covid19_drdfm/data/example-data"
+run_paths = Path(run_paths_string)
+path_to_results = Path(st.text_input("Path directory of runs", value=run_paths_string))
 df = parse_multiple_runs(path_to_results)
 
 
@@ -53,19 +55,42 @@ def create_plot(df):
     st.plotly_chart(fig, use_container_width=True)
     return metric
 
-
-def get_summary(df):
+# Function to get number of failed states
+def count_failed_states(run_name):
+    failed_states_count = 0
+    failed_file_path = run_paths / run_name / "failed.txt"
+    if failed_file_path.exists():
+        with open(failed_file_path, 'r') as failed_file:
+            for line in failed_file:
+                if "Matrix is not positive definite" in line:
+                    failed_states_count += 1
+    return failed_states_count 
+
+# Function to calculate deviation from the minimum number of failed states for a specific run
+def calculate_deviation(run_name):
+    min_failed_states = float('inf')
+    for run_path in run_paths.iterdir():
+        if run_path.is_dir():
+            failed_states_count = count_failed_states(run_path.name)
+            min_failed_states = min(min_failed_states, failed_states_count)
+    
+    failed_states_count = count_failed_states(run_name)
+    deviation = - (failed_states_count - min_failed_states)
+    return deviation
+
+def get_summary(df, run_name):
     # Median metrics
-    col1, col2, col3 = st.columns(3)
-    col1.metric("Median Log Likelihood", df["Log Likelihood"].median())
-    col2.metric("Median AIC", df["AIC"].median())
-    col3.metric("Median EM Iterations", df["EM Iterations"].median())
+    col1, col2, col3, col4 = st.columns(4)
+    col1.metric("Number of Failed States", count_failed_states(run_name), calculate_deviation(run_name))
+    col2.metric("Median Log Likelihood", df["Log Likelihood"].median())
+    col3.metric("Median AIC", df["AIC"].median())
+    col4.metric("Median EM Iterations", df["EM Iterations"].median())
 
 
 def show_summary(df):
     run = st.selectbox("Select a run", df["Run"].unique())
     filtered_df = df[(df["Run"] == run)]
-    return get_summary(filtered_df)
+    return get_summary(filtered_df, run)
 
 
 def run_normal(df, metric):