Natural Language Processing in Diagnostic Texts from Nephropathology

Association between chiropractic spinal manipulation and cauda equina syndrome in adults with low back pain: Retrospective cohort study of US academic health centers

BACKGROUND

Cauda equina syndrome (CES) is a lumbosacral surgical emergency that has been associated with chiropractic spinal manipulation (CSM) in case reports. However, identifying if there is a potential causal effect is complicated by the heightened incidence of CES among those with low back pain (LBP). The study hypothesis was that there would be no increase in the risk of CES in adults with LBP following CSM compared to a propensity-matched cohort following physical therapy (PT) evaluation without spinal manipulation over a three-month follow-up period.

METHODS

A query of a United States network (TriNetX, Inc.) was conducted, searching health records of more than 107 million patients attending academic health centers, yielding data ranging from 20 years prior to the search date (July 30, 2023). Patients aged 18 or older with LBP were included, excluding those with pre-existing CES, incontinence, or serious pathology that may cause CES. Patients were divided into two cohorts: (1) LBP patients receiving CSM or (2) LBP patients receiving PT evaluation without spinal manipulation. Propensity score matching controlled for confounding variables associated with CES.

RESULTS

67,220 patients per cohort (mean age 51 years) remained after propensity matching. CES incidence was 0.07% (95% confidence intervals [CI]: 0.05-0.09%) in the CSM cohort compared to 0.11% (95% CI: 0.09-0.14%) in the PT evaluation cohort, yielding a risk ratio and 95% CI of 0.60 (0.42-0.86; p = .0052). Both cohorts showed a higher rate of CES during the first two weeks of follow-up.

CONCLUSIONS

These findings suggest that CSM is not a risk factor for CES. Considering prior epidemiologic evidence, patients with LBP may have an elevated risk of CES independent of treatment. These findings warrant further corroboration. In the meantime, clinicians should be vigilant to identify LBP patients with CES and promptly refer them for surgical evaluation.

Association between spinal manipulative therapy and lumbar spine reoperation after discectomy: a retrospective cohort study

BACKGROUND

Patients who undergo lumbar discectomy may experience ongoing lumbosacral radiculopathy (LSR) and seek spinal manipulative therapy (SMT) to manage these symptoms. We hypothesized that adults receiving SMT for LSR at least one year following lumbar discectomy would be less likely to undergo lumbar spine reoperation compared to matched controls not receiving SMT, over two years' follow-up.

METHODS

We searched a United States network of health records (TriNetX, Inc.) for adults aged ≥ 18 years with LSR and lumbar discectomy ≥ 1 year previous, without lumbar fusion or instrumentation, from 2003 to 2023. We divided patients into two cohorts: (1) chiropractic SMT, and (2) usual care without chiropractic SMT. We used propensity matching to adjust for confounding variables associated with lumbar spine reoperation (e.g., age, body mass index, nicotine dependence), calculated risk ratios (RR), with 95% confidence intervals (CIs), and explored cumulative incidence of reoperation and the number of SMT follow-up visits.

RESULTS

Following propensity matching there were 378 patients per cohort (mean age 61 years). Lumbar spine reoperation was less frequent in the SMT cohort compared to the usual care cohort (SMT: 7%; usual care: 13%), yielding an RR (95% CIs) of 0.55 (0.35-0.85; P = 0.0062). In the SMT cohort, 72% of patients had ≥ 1 follow-up SMT visit (median = 6).

CONCLUSIONS

This study found that adults experiencing LSR at least one year after lumbar discectomy who received SMT were less likely to undergo lumbar spine reoperation compared to matched controls not receiving SMT. While these findings hold promise for clinical implications, they should be corroborated by a prospective study including measures of pain, disability, and safety to confirm their relevance. We cannot exclude the possibility that our results stem from a generalized effect of engaging with a non-surgical clinician, a factor that may extend to related contexts such as physical therapy or acupuncture.

REGISTRATION

Open Science Framework ( https://osf.io/vgrwz ).

Natural Language Processing Can Automate Extraction of Barrett's Esophagus Endoscopy Quality Metrics

Objectives

To develop an automated natural language processing (NLP) method for extracting high-fidelity Barrett's Esophagus (BE) endoscopic surveillance and treatment data from the electronic health record (EHR).

Methods

Patients who underwent BE-related endoscopies between 2016 and 2020 at a single medical center were randomly assigned to a development or validation set. Those not aged 40 to 80 and those without confirmed BE were excluded. For each patient, free text pathology reports and structured procedure data were obtained. Gastroenterologists assigned ground truth labels. An NLP method leveraging MetaMap Lite generated endoscopy-level diagnosis and treatment data. Performance metrics were assessed for this data. The NLP methodology was then adapted to label key endoscopic eradication therapy (EET)-related endoscopy events and thereby facilitate calculation of patient-level pre-EET diagnosis, endotherapy time, and time to CE-IM.

Results

99 patients (377 endoscopies) and 115 patients (399 endoscopies) were included in the development and validation sets respectively. When assigning high-fidelity labels to the validation set, NLP achieved high performance (recall: 0.976, precision: 0.970, accuracy: 0.985, and F1-score: 0.972). 77 patients initiated EET and underwent 554 endoscopies. Key EET-related clinical event labels had high accuracy (EET start: 0.974, CE-D: 1.00, and CE-IM: 1.00), facilitating extraction of pre-treatment diagnosis, endotherapy time, and time to CE-IM.

Conclusions

High-fidelity BE endoscopic surveillance and treatment data can be extracted from routine EHR data using our automated, transparent NLP method. This method produces high-level clinical datasets for clinical research and quality metric assessment.

Editorial on Special Issue "Artificial Intelligence in Pathological Image Analysis"

The artificial intelligence (AI), especially deep learning models, is highly compatible with medical images and natural language processing and is expected to be applied to pathological image analysis and other medical fields [...].