A clinical prediction rule for meniscal tears in primary care: development and internal validation using a multicentre study

Digital Health Applications to Establish a Remote Diagnosis of Orthopedic Knee Disorders: Scoping Review

BACKGROUND

Knee pain is highly prevalent worldwide, and this number is expected to rise in the future. The COVID-19 outbreak, in combination with the aging population, rising health care costs, and the need to make health care more accessible worldwide, has led to an increasing demand for digital health care applications to deliver care for patients with musculoskeletal conditions. Digital health and other forms of telemedicine can add value in optimizing health care for patients and health care providers. This might reduce health care costs and make health care more accessible while maintaining a high level of quality. Although expectations are high, there is currently no overview comparing digital health applications with face-to-face contact in clinical trials to establish a primary knee diagnosis in orthopedic surgery.

OBJECTIVE

This study aimed to investigate the currently available digital health and telemedicine applications to establish a primary knee diagnosis in orthopedic surgery in the general population in comparison with imaging or face-to-face contact between patients and physicians.

METHODS

A scoping review was conducted using the PubMed and Embase databases according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. The inclusion criteria were studies reporting methods to determine a primary knee diagnosis in orthopedic surgery using digital health or telemedicine. On April 28 and 29, 2021, searches were conducted in PubMed (MEDLINE) and Embase. Data charting was conducted using a predefined form and included details on general study information, study population, type of application, comparator, analyses, and key findings. A risk-of-bias analysis was not deemed relevant considering the scoping review design of the study.

RESULTS

After screening 5639 articles, 7 (0.12%) were included. In total, 2 categories to determine a primary diagnosis were identified: screening studies (4/7, 57%) and decision support studies (3/7, 43%). There was great heterogeneity in the included studies in algorithms used, disorders, input parameters, and outcome measurements. No more than 25 knee disorders were included in the studies. The included studies showed a relatively high sensitivity (67%-91%). The accuracy of the different studies was generally lower, with a specificity of 27% to 48% for decision support studies and 73% to 96% for screening studies.

CONCLUSIONS

This scoping review shows that there are a limited number of available applications to establish a remote diagnosis of knee disorders in orthopedic surgery. To date, there is limited evidence that digital health applications can assist patients or orthopedic surgeons in establishing the primary diagnosis of knee disorders. Future research should aim to integrate multiple sources of information and a standardized study design with close collaboration among clinicians, data scientists, data managers, lawyers, and service users to create reliable and secure databases.

Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention

PURPOSE

This study sought to develop and internally validate a machine learning model to identify risk factors and quantify overall risk of secondary meniscus injury in a longitudinal cohort after primary ACL reconstruction (ACLR).

METHODS

Patients with new ACL injury between 1990 and 2016 with minimum 2-year follow-up were identified. Records were extensively reviewed to extract demographic, treatment, and diagnosis of new meniscus injury following ACLR. Four candidate machine learning algorithms were evaluated to predict secondary meniscus tears. Performance was assessed through discrimination using area under the receiver operating characteristics curve (AUROC), calibration, and decision curve analysis; interpretability was enhanced utilizing global variable importance plots and partial dependence curves.

RESULTS

A total of 1187 patients underwent ACLR; 139 (11.7%) experienced a secondary meniscus tear at a mean time of 65 months post-op. The best performing model for predicting secondary meniscus tear was the random forest (AUROC = 0.790, 95% CI: 0.785-0.795; calibration intercept = 0.006, 95% CI: 0.005-0.007, calibration slope = 0.961 95% CI: 0.956-0.965, Brier's score = 0.10 95% CI: 0.09-0.12), and all four machine learning algorithms outperformed traditional logistic regression. The following risk factors were identified: shorter time to return to sport (RTS), lower VAS at injury, increased time from injury to surgery, older age at injury, and proximal ACL tear.

CONCLUSION

Machine learning models outperformed traditional prediction models and identified multiple risk factors for secondary meniscus tears after ACLR. Following careful external validation, these models can be deployed to provide real-time quantifiable risk for counseling and timely intervention to help guide patient expectations and possibly improve clinical outcomes.

LEVEL OF EVIDENCE

III.

Diagnostic Validity of Combining History Elements and Physical Examination Tests for Traumatic and Degenerative Symptomatic Meniscal Tears

BACKGROUND

The current approach to the clinical diagnosis of traumatic and degenerative symptomatic meniscal tears (SMTs) proposes combining history elements and physical examination tests without systematic prescription of imaging investigations, yet the evidence to support this diagnostic approach is scarce.

OBJECTIVE

To assess the validity of diagnostic clusters combining history elements and physical examination tests to diagnose or exclude traumatic and degenerative SMT compared with other knee disorders.

DESIGN

Prospective diagnostic accuracy study.

SETTINGS

Patients were recruited from 2 orthopedic clinics, 2 family medicine clinics, and from a university community.

PATIENTS

A total of 279 consecutive patients who underwent consultation for a new knee complaint.

METHODS

Each patient was assessed independently by 2 evaluators. History elements and standardized physical examination tests performed by a physiotherapist were compared with the reference standard: an expert physicians' composite diagnosis including a clinical examination and confirmatory magnetic resonance imaging. Participating expert physicians were orthopedic surgeons (n = 3) or sport medicine physicians (n = 2). Penalized logistic regression (least absolute shrinkage and selection operator) was used to identify history elements and physical examination tests associated with the diagnosis of SMT and recursive partitioning was used to develop diagnostic clusters.

MAIN OUTCOME MEASURES

Diagnostic accuracy measures were calculated including sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios (LR+/-) with associated 95% confidence intervals (CIs).

RESULTS

Eighty patients had a diagnosis of SMT (28.7%), including 35 traumatic tears and 45 degenerative tears. The combination a history of trauma during a pivot, medial knee pain location, and a positive medial joint line tenderness test was able to diagnose (LR+ = 8.9; 95% CI 6.1-13.1) or exclude (LR- = 0.10; 95% CI 0.03-0.28) a traumatic SMT. Combining a history of progressive onset of pain, medial knee pain location, pain while pivoting, absence of valgus or varus knee misalignment, or full passive knee flexion was able to moderately diagnose (LR+ = 6.4; 95% CI 4.0-10.4) or exclude (LR- = 0.10; 95% CI 0.03-0.31) a degenerative SMT. Internal validation estimates were slightly lower for all clusters but demonstrated positive LR superior to 5 and negative LR inferior to 0.2 indicating moderate shift in posttest probability.

CONCLUSION

Diagnostic clusters combining history elements and physical examination tests can support the differential diagnosis of SMT. These results represent the initial derivation of the clusters and external validation is mandatory.

LEVEL OF EVIDENCE

I.

Anamnestic prediction of bucket handle compared to other tear patterns of the medial meniscus in stable knees

Objective

The aim of this study was to analyze and compare the preoperative anamnestic details between patients with an arthroscopic diagnosis of bucket handle and other tear patterns of the medial meniscus in stable knees.

Methods

A total of 204 patients (mean age 49.3 ± 13 years) were included in the study. The mean age was 49.3 ± 13 years. The study group included 65 patients (63 males, 2 females) with an arthroscopic diagnosis of bucket handle tear and the control group included 139 patients (90 males, 49 females) with non-bucket handle tear patterns. The preoperative clinical assessments of the two groups were analyzed retrospectively. Anamnestic prediction for the diagnosis of a bucket handle tear was based upon various medical history parameters. Multivariate logistic regression was carried out to identify independent anamnestic factors for predicting isolated bucket handle tears of the medial meniscus compared to non-bucket handle tears.

Results

Analysis of the multivariate logistic regression yielded 3 statistically significant independent anamnestic risk factors for predicting isolated bucket handle tears of the medial meniscus: male gender (OR, 9.7; 95% CI, 1.1–37.6), locking events (OR, 4.6; 95% CI, 1.8–11.3) and pain in extension (OR, 6.9; 95% CI, 2.5–23.7). Other preoperative variables such as age, BMI, activity level, comorbidities, duration of symptoms, pain location, preceding injury and its mechanism had no significant effect on tear pattern.

Conclusions

Preoperative strong clues for bucket handle tears of the medial meniscus in stable knees are male gender, locking events and limitation in extension.

Level of evidence: Level III, Diagnostic study.

Severity of Carpal Tunnel Syndrome and Diagnostic Accuracy of Hand and Body Anthropometric Measures

Objective

To study the diagnostic properties of hand/wrist and body measures according to validated clinical and electrophysiological carpal tunnel syndrome (CTS) severity scales.

Methods

We performed a prospective case-control study. For each case, two controls were enrolled. Two five-stage clinical and electrophysiological scales were used to evaluate CTS severity. Anthropometric measurements were collected and obesity indicators and hand/wrist ratios were calculated. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and likelihood ratios were calculated separately by gender.

Results

We consecutively enrolled 370 cases and 747 controls. The wrist-palm ratio, waist-hip-height ratio and waist-stature ratio showed the highest proportion of cases with abnormal values in the severe stages of CTS for clinical and electrophysiological severity scales in both genders. Accuracy tended to increase with CTS severity for females and males. In severe stage, most of the indexes presented moderate accuracy in both genders. Among subjects with severe CTS, the wrist-palm ratio presented the highest AUC for hand measures in the clinical and electrophysiological severity scales both in females (AUC 0.83 and 0.76, respectively) and males (AUC 0.91 and 0.82, respectively). Among subjects with severe CTS, the waist-stature ratio showed the highest AUC for body measures in the clinical and electrophysiological severity scales both in females (AUC 0.78 and 0.77, respectively) and males (AUC 0.84 and 0.76, respectively). The results of waist-hip-height ratio AUC were similar.

Conclusions

Wrist-palm ratio, waist-hip-height ratio and waist-stature ratio could contribute to support the diagnostic hypothesis of severe CTS that however has to be confirmed by nerve conduction study.