According to the reporter, on (b)(6) 2026, post-operative of a right colectomy under coelio performed on (b)(6) 2026, the patient had an emergency CT (computed tomography) scan due to abdominal pain, rectorrhagia, and hyperthermia. The patient had anterior anastomotic release with localized peritonitis possibly due to anastomotic bleeding. The patient underwent re-operation on (b)(6) 2026 to resolve the issue. The patient was put on antibiotics. The patient was discharged on (b)(6) 2026.
MAUDE Signal Explorer
Surgical Staplers · A Human Factors Lens
An independent demo by Kennedy DeSousa
Post-market surveillance, read like a human factors engineer
What 198,000+ adverse-event reports say about surgical staplers
HFE teams mostly look forward — formative studies, validation, design controls. But the FDA's MAUDE database is a backward-looking goldmine: real use errors, in real ORs, in the reporters' own words. This page mines the public openFDA device-event API for surgical staplers (product codes GAG & GDW) and shows how post-market signals can seed formative-study hypotheses.
197,585
Total reports in MAUDE
156,659
Malfunctions
38,718
Injuries
1,378
Deaths
Live from openFDA · dataset last updated 2026-09-08
Reports per year — and the hidden-database story
For years, stapler manufacturers could route adverse events through FDA's “Alternative Summary Reporting” program — tens of thousands of malfunction reports that never appeared in public MAUDE. After investigative reporting surfaced the practice, FDA ended ASR in mid-2019 and the hidden reports flooded into the public record. The lesson for anyone reading this data: the shape of a reporting curve reflects policy as much as risk.
A use-error lens on the narratives
Event narratives often encode perception, cognition, or action failures — the raw material of use-related risk analysis. These phrase counts are a deliberately simple heuristic for surfacing candidate reports to read, not a validated classifier:
Failed to fire
1,047
reports mentioning “failed to fire”
Boundary caseOften entangled with loading, positioning, or tissue-thickness selection — but a jammed mechanism with perfect technique is a device failure. Run the perfect-device test before claiming it.
Difficult to remove
996
reports mentioning “difficult to remove”
ActionPost-fire release problems often involve technique interaction with the release design
Misfire
853
reports mentioning “misfire”
ActionFrequently involves firing sequence or reload handling — execution of a known procedure
Inadvertent action
499
reports mentioning “inadvertently”
ActionMarker for unintended activation or release — classic slip during execution
Labeled 'user error'
178
reports mentioning “user error”
Reporter-attributedHow reporters themselves attribute the event — attribution, not analysis; treat as a pointer, not a classification
Wrong size
24
reports mentioning “wrong size”
PerceptionCartridge/tissue mismatch — the user didn't detect or judge the tissue thickness the design asked them to assess
Which analysis owns this cause?
A signal only becomes a use-relatedrisk when the initiating event is a human perceiving, deciding, or acting while the device performs to specification. Device deviates from spec → that's a failure mode for the FMEA family. Two tests sort every narrative:
1 · The perfect-device test
Would this scenario still occur with a flawlessly functioning device? Yes → use-related analysis. No— it requires a malfunction → failure-mode analysis. A “failed to fire” narrative can land either way, which is why it's tagged a boundary case above.
2 · The initiating-event test
When a chain involves both a failure and a user, ask what starts it. The failure itself belongs to the failure-mode analyses — but the user-response task the failure creates (respond, recover, replace) is use-related risk in its own right.
The latest reports, in the reporters' own words
The most recent stapler narratives in MAUDE, with use-error phrases highlighted. Reading raw narratives is where the method earns its keep — counts point you somewhere; the words tell you why.
It was reported that during a da Vinci-assisted pulmonary lobectomy surgical procedure, the White SureForm 45 Reload staple line bled after a misfire; the staple line was incomplete. The site reported being required to manually select the Reload color from the Surgeon Side Console before the fire. The SureForm 45 Curved-Tip Stapler was replaced with a backup stapler. After a successful firing sequence, no error messages were observed; the White staple line bled and required intervention. The surgeon reports the level of bleeding produced from the staple line was unexpected; the estimated blood loss was not provided. The bleeding was controlled intraoperatively; the procedure was completed robotically with a backup reload.
It was reported that, during an unknown surgery, the first firing was completed without issue. However, during the second firing, the firing knob did not move. Another device was used to complete the case. There were no adverse consequences to the patient. No further information is available.
It was reported that during an unknown surgery ,the device could not be fired. Another device was used to complete the case. There were no adverse consequences to the patient. No further information is available.
It was reported that during an Unknown surgery, after fired, noted the staples malformed. Used suture to oversew. There was no patient consequence reported. No additional information can be provided.
Why this matters for HFE teams
1 · Signal
Trend breaks and phrase clusters point to where users struggle — before your own study budget is spent.
2 · Hypothesis
Each recurring narrative pattern (“wrong size,” “failed to fire”) becomes a candidate use error for task analysis and PCA classification.
3 · Study design
Formative scenarios and IFU probes get grounded in documented field failures instead of conference-room guesses.
4 · Risk file
Confirmed patterns flow into the use-related risk analysis — recalibrating severity and likelihood on existing rows, seeding new ones, and tracing each use-error chain to the hazards the top-level risk file owns.
Limitations — read before drawing conclusions
- MAUDE has no denominator. Report counts can't be turned into rates — procedure volumes aren't in the data, so more reports ≠ more dangerous.
- Reporting is biased and incomplete. Underreporting is well documented; media attention, litigation, and policy changes (see the ASR story above) all move the curve.
- Keyword matching is a heuristic. Phrase counts surface candidates for human reading. A validated use-error classification needs trained reviewers and a coding scheme (e.g., PCA taxonomy) with reliability checks.
- Duplicates and follow-ups exist. The same event can generate multiple MDRs; no deduplication is attempted here.
- Narratives are secondhand. Most are written by manufacturers from user communications, with their own framing incentives.