Incidental Durotomy Following Surgery for Degenerative Lumbar Disease and the Impact of Minimally Invasive Surgical Technique on the Rate and Need for Surgical Revision: A Case Series

Subdural Extra-arachnoid Hygroma Because of Occult Distal Durotomy After Minimally Invasive Decompression: A Case Report

CASE

We present the case of a 59-year-old man who had MIS L4-5 decompression. He presented the next day with intractable back and leg pain. Magnetic resonance imaging revealed ventral displacement of the cauda equina and a subdural collection on the right L3/L4 nerve roots. Revision decompression revealed occult durotomy caudal and contralateral to the index decompression.

CONCLUSION

Minimally invasive spine (MIS) surgery leverages shorter operative time and reduced postoperative pain. Yet, decreased exposure can make identification and management complications challenging. This report highlights occult durotomy and spinal subdural extra-arachnoid hygroma in patients with postoperative nerve compression after seemingly uncomplicated MISS.

Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery

Background

The aim of this study was to develop natural language processing (NLP) algorithms to conduct automated identification of incidental durotomy, wound drains, and the use of sutures or skin clips for wound closure, in free text operative notes of patients following lumbar surgery.

Methods

A single-centre retrospective case series analysis was conducted between January 2015 and June 2022, analysing operative notes of patients aged >18 years who underwent a primary lumbar discectomy and/or decompression at any lumbar level. Extreme gradient-boosting NLP algorithms were developed and assessed on five performance metrics: accuracy, area under receiver-operating curve (AUC), positive predictive value (PPV), specificity, and Brier score.

Results

A total of 942 patients were used in the training set and 235 patients, in the testing set. The average age of the cohort was 53.900 ± 16.153 years, with a female predominance of 616 patients (52.3%). The models achieved an aggregate accuracy of >91%, a specificity of >91%, a PPV of >84%, an AUC of >0.933, and a Brier score loss of ≤0.082. The decision curve analysis also revealed that these NLP algorithms possessed great clinical net benefit at all possible threshold probabilities. Global and local model interpretation analyses further highlighted relevant clinically useful features (words) important in classifying the presence of each entity appropriately.

Conclusions

These NLP algorithms can help monitor surgical performance and complications in an automated fashion by identifying and classifying the presence of various intra-operative elements in lumbar spine surgery.