diff --git a/VERSIONLOG.md b/VERSIONLOG.md
index dec071ff..53e89cb5 100644
--- a/VERSIONLOG.md
+++ b/VERSIONLOG.md
@@ -1,5 +1,9 @@
 # Scilifelab_epps Version Log
 
+## 20241006.1
+
+Fix issue with empty Aviti runmanifest results in Lane nr 0
+
 ## 20241002.1
 
 Fix bug with index checker EPP with preset index sets
diff --git a/scripts/aviti_run_parameter_parser.py b/scripts/aviti_run_parameter_parser.py
index 19bd402a..20268681 100644
--- a/scripts/aviti_run_parameter_parser.py
+++ b/scripts/aviti_run_parameter_parser.py
@@ -157,33 +157,32 @@ def calculate_mean(input_list, key):
 
 
 def set_run_stats(process, run_dir):
-    global lane_stats
     run_stats = parse_run_stats(run_dir)
     for art in process.all_outputs():
         if "Lane" in art.name:
             lane_nbr = int(art.name.split(" ")[1])
-            lane_stats = next(
-                d for d in run_stats["LaneStats"] if d["Lane"] == lane_nbr
-            )
-        for read in lane_stats["Reads"]:
-            read_key = read["Read"]
-            art.udf[f"Reads PF (M) {read_key}"] = lane_stats["PFCount"] / 1000000
-            art.udf[f"%PF {read_key}"] = lane_stats["PercentPF"]
-            art.udf[f"Yield PF (Gb) {read_key}"] = lane_stats["TotalYield"] / 1000000000
-            art.udf[f"% Aligned {read_key}"] = read["PhiXAlignmentRate"]
-            art.udf[f"% Bases >=Q30 {read_key}"] = calculate_mean(
-                read["Cycles"], "PercentQ30"
-            )
-            art.udf[f"% Bases >=Q40 {read_key}"] = calculate_mean(
-                read["Cycles"], "PercentQ40"
-            )
-            art.udf[f"Avg Q Score {read_key}"] = calculate_mean(
-                read["Cycles"], "AverageQScore"
-            )
-            art.udf[f"% Error Rate {read_key}"] = calculate_mean(
-                read["Cycles"], "PercentPhixErrorRate"
-            )
-        art.put()
+            lane_stats = run_stats["LaneStats"][lane_nbr - 1]
+            for read in lane_stats["Reads"]:
+                read_key = read["Read"]
+                art.udf[f"Reads PF (M) {read_key}"] = lane_stats["PFCount"] / 1000000
+                art.udf[f"%PF {read_key}"] = lane_stats["PercentPF"]
+                art.udf[f"Yield PF (Gb) {read_key}"] = (
+                    lane_stats["TotalYield"] / 1000000000
+                )
+                art.udf[f"% Aligned {read_key}"] = read["PhiXAlignmentRate"]
+                art.udf[f"% Bases >=Q30 {read_key}"] = calculate_mean(
+                    read["Cycles"], "PercentQ30"
+                )
+                art.udf[f"% Bases >=Q40 {read_key}"] = calculate_mean(
+                    read["Cycles"], "PercentQ40"
+                )
+                art.udf[f"Avg Q Score {read_key}"] = calculate_mean(
+                    read["Cycles"], "AverageQScore"
+                )
+                art.udf[f"% Error Rate {read_key}"] = calculate_mean(
+                    read["Cycles"], "PercentPhixErrorRate"
+                )
+            art.put()
     process.put()