Ankle fractures: a systematic review of patient-reported outcome measures and their measurement properties

PURPOSE

Ankle fractures are commonly occurring fractures, especially in the aging population, where they often present as fragility fractures. The disease burden and economic costs to the patient and society are considerable. Choosing accurate outcome measures for the evaluation of the management of ankle fractures in clinical trials facilitates better decision-making. This systematic review assesses the evidence for the measurement properties of patient-reported outcome measures (PROMs) used in the evaluation of adult patients with ankle fractures.

METHODS

Searches were performed in CINAHL, EMBASE, Medline and Google Scholar from the date of inception to July 2021. Studies that assessed the measurement properties of a PROM in an adult ankle fracture population were included. The included studies were assessed according to the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology for systematic reviews of PROMs.

RESULTS

In total, 13 different PROMs were identified in the 23 included articles. Only the Ankle Fracture Outcome of Rehabilitation Measure (A-FORM) presented some evidence on content validity. The Olerud-Molander Ankle Score (OMAS) and Self-reported Foot and Ankle Score (SEFAS) displayed good evidence of construct validity and internal consistency. The measurement properties of the OMAS, LEFS and SEFAS were most studied.

CONCLUSION

The absence of validation studies covering all measurement properties of PROMs used in the adult ankle fracture population precludes the recommendation of a specific PROM to be used in the evaluation of this population. Further research should focus on validation of the content validity of the instruments used in patients with ankle fractures.

Changing from a two-tiered to a one-tiered trauma team activation protocol: a before-after observational cohort study investigating the clinical impact of undertriage

BACKGROUND

The aim of this study was to compare the effect of the change in TTA protocol from a two-tier to one-tier, with focus on undertriage and mortality.

MATERIAL AND METHODS

A before-after observational cohort study based on data extracted from the Stavanger University Hospital Trauma registry in the transition period from two-tier to a one-tier TTA protocol over two consecutive 1-year periods (2017-2018). Comparative analysis was done between the two time-periods for descriptive characteristics and outcomes. The main outcomes of interest were undertriage and mortality.

RESULTS

During the study period 1234 patients were included in the registry, of which 721 (58%) were in the two-tier and 513 (42%) in the one-tier group. About one in five patients (224/1234) were severely injured (ISS > 15). Median age was 39 in the two-tier period and 43 years in the one-tier period (p = 0.229). Median ISS was 5 for the two-tier period vs 9, in the one-tier period (p = 0.001). The undertriage of severely injured patients in the two-tier period was 18/122 (15%), compared to 31/102 (30%) of patients in the one-tier period (OR = 2.5; 95% CI 1.8-4.52). Overall mortality increased significantly between the two TTA protocols, from 2.5 to 4.7% (p = 0.033), OR 0.51 (0.28-0.96) CONCLUSION: A protocol change from two-tiered TTA to one-tiered TTA increased the undertriage in our trauma system. A two-tiered TTA may be beneficial for better patient care.

Awareness of age-related changes in Norwegian individuals 50+. Short form questionnaire validation

Background

A questionnaire assessing awareness of positive and negative age-related changes (AARC gains and losses) was developed in the US and Germany, and validated for the UK and Brazilian populations. In this study, we validated the short-form measure (AARC-10 SF) in the Norwegian population aged 50 and over. In addition, the relationship between cognitive variables and AARC was examined.

Methods

Cross-sectional analyses of data from 1,510 participants in the ongoing online PROTECT Norge study were used to explore and confirm the two-factor structure of AARC gains and AARC losses; reliability; measurement invariance across different population groups defined by sex, education level, employment, and in middle age, early old age, and advanced old age. We explored the relationship between AARC and demographic variables (defined in the same way as the population groups).

Results

We confirmed the two-factor structure (gains and losses) of the Norwegian translation of the AARC-10 SF. We did not find mutual correlations between related items in gains and losses, except for the physical health item from the gains dimension, which was positively correlated with all items of the losses dimension. Age, sex, marital status, employment, and university education predicted AARC gains and losses.

Conclusion

The Norwegian translation of the AARC-10 SF captures individuals' positive and negative self-perceptions of age-related changes in their mental, physical, and cognitive health.

Prognostic value of disseminated tumor cells in unresectable pancreatic ductal adenocarcinoma: a prospective observational study

BACKGROUND

Although pancreatic ductal adenocarcinoma (PDAC) rarely metastasizes to the skeleton, disseminated tumor cells have been detected in bone marrow samples from patients with this disease. The prognostic value of such findings is currently unclear. Thus, the current study aimed to clarify the prognostic information associated with disseminated tumor cell detection in samples from patients with PDAC.

METHODS

Bone marrow aspirates were obtained from 48 patients with locally advanced (n = 11) or metastatic (n = 37) PDAC, before and after 2 months of chemotherapy. Disseminated tumor cells were detected with an mRNA panel and quantitative reverse transcription PCR. We used the highest levels measured in healthy bone marrow (n = 30) as a threshold to define the positive detection of disseminated tumor cells. Progression-free and overall survival were analyzed with Kaplan-Meier and Cox proportional hazards regression analyses.

RESULTS

Disseminated tumor cells were detected in 15/48 (31%) bone marrow samples obtained before starting chemotherapy and in 8/25 (32%) samples obtained during chemotherapy. Patients with disseminated tumor cells detected before therapy had significantly shorter progression-free (p = 0.03; HR = 2.0) and overall survival (p = 0.03; HR = 2.0), compared to those without disseminated tumor cells in the bone marrow. When restricting disseminated tumor cell detection to keratins KRT7 and KRT8, the prognostic information was substantially stronger (p = 1 × 10-6; HR = 22, and p = 2 × 10-5; HR = 7.7, respectively). The multivariable Cox regression analysis demonstrated that disseminated tumor cell detection prior to treatment had independent prognostic value. In contrast, disseminated tumor cells detected during treatment did not have prognostic value.

CONCLUSIONS

Disseminated tumor cells detected before commencing chemotherapy had prognostic value in patients with inoperable PDAC.

Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation

OBJECTIVES

To estimate the uncertainty of a deep learning (DL)-based prostate segmentation algorithm through conformal prediction (CP) and to assess its effect on the calculation of the prostate volume (PV) in patients at risk of prostate cancer (PC).

METHODS

Three-hundred seventy-seven multi-center 3-Tesla axial T2-weighted exams from biopsied males (66.64  ±  7.47 years) at risk of PC were retrospectively included in the study. Assessment of PV based on PI-RADS 2.1 ellipsoid formula (PV r e f ) was available for included patients. Prostate segmentations were obtained from a DL model and used to calculate the PV (PV D L ). CP was applied at a confidence level of 85% to flag unreliable pixel segmentations of the DL model. Subsequently, the PV (PV C P ) was calculated when disregarding uncertain pixel segmentations. Agreement betweenPV D L andPV C P was evaluated against the reference standardPV r e f . Intraclass correlation coefficient (ICC) and Bland-Altman plots were used to assess the agreement. The relative volume difference (RVD) was used to evaluate the PV calculation accuracy, and the Wilcoxon Signed-Rank Test was used to assess statistical differences. A p-value < 0.05 was considered statistically significant.

RESULTS

Conformal prediction significantly reduced RVD when compared to the DL algorithm (RVD = - 2.81  ±  8.85 and RVD = -8.01  ±  11.50).PV C P showed a significantly larger agreement thanPV D L when using the reference standardPV r e f (mean difference (95% limits of agreement)PV C P : 1.27 mL (- 13.64; 16.17 mL)PV D L : 6.07 mL (- 14.29; 26.42 mL)), with an excellent ICC (PV C P : 0.97 (95% CI: 0.97 to 0.98)).

CONCLUSION

Uncertainty quantification through CP increases the accuracy and reliability of DL-based PV assessment in patients at risk of PC.

CRITICAL RELEVANCE STATEMENT

Conformal prediction can flag uncertain pixel predictions of DL-based prostate MRI segmentation at a desired confidence level, increasing the reliability and safety of prostate volume assessment in patients at risk of prostate cancer.

KEY POINTS

Conformal prediction can flag uncertain pixel predictions of prostate segmentations at a user-defined confidence level. Deep learning with conformal prediction shows high accuracy in prostate volumetric assessment. Agreement between automatic and ellipsoid-derived volume was significantly larger with conformal prediction.

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

BACKGROUND

Aging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age-related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored.

PURPOSE

To develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence.

STUDY TYPE

Retrospective.

POPULATION

Four hundred and sixty-eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low-grade PC (Gleason score ≤6) and 131 negative patients, defined as non-csPC. The model was tested on 90 negative, 52 low-grade (142 non-csPC), and 114 csPC patients.

FIELD STRENGTH/SEQUENCE

3-T, axial T2-weighted spin sequence.

ASSESSMENT

Chronological age was defined as the age of the participant at the time of the visit. Prostate-specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non-csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non-csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI-RADS ≥ 3 score.

STATISTICAL TESTS

T-test, Mann-Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant.

RESULTS

After adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32-6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non-csPC than that of adjusted PI-RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975-0.987).

DATA CONCLUSION

PAG may be associated with the risk of csPC and could outperform other PC risk factors.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Podocan and Adverse Clinical Outcome in Patients Admitted With Suspected Acute Coronary Syndromes

Background

Markers of bone and extracellular matrix (ECM) remodeling may be associated with adverse outcomes in atherosclerotic cardiovascular disease. Podocan is a newly discovered ECM glycoprotein, previously not studied in a chest pain population. We wanted to study the association between Podocan levels on admission and the risk of adverse outcomes in a chest pain population with suspected acute coronary syndromes.

Methods

A total of 815 patients from the Risk markers in Acute Coronary Syndrome (RACS) trial with suspected coronary chest pain were followed for 7 years. Blood samples were taken immediately after inclusion and stored in the biobank. Associations between Podocan and endpoints were assessed with Cox proportional hazards analyses.

Results

The median admission level of Podocan was 0.674 ng/ml (0.566–0.908 ng/ml). No significant association was found between Podocan quartile levels and all-cause death, neither at 1 year nor 2- or 7-years follow-up (p > 0.05 for all). Furthermore, no significant association could be shown between Podocan and cardiac death, myocardial infarction (MI), stroke, or the composites of all-cause death/MI/stroke or cardiac death/MI/stroke (p > 0.05 for all). Similarly, in a subgroup of patients with Troponin T-positive (n = 432) there was no significant association between Podocan and any of the outcome measures (p > 0.05 for all endpoints and points in time).

Conclusion

Podocan, a novel ECM biomarker, is not associated with all-cause mortality or other major cardiovascular adverse events in patients admitted with acute chest pain suspected to be of coronary origin.

Clinical Trials.gov Identifier:

NCT00521976.

Analysis and knowledge extraction of newborn resuscitation activities from annotation files

Deprivation of oxygen in an infant during and after birth leads to birth asphyxia, which is considered one of the leading causes of death in the neonatal period. Adequate resuscitation activities are performed immediately after birth to save the majority of newborns. The primary resuscitation activities include ventilation, stimulation, drying, suction, and chest compression. While resuscitation guidelines exist, little research has been conducted on measured resuscitation episodes. Objective data collected for measuring and registration of the executed resuscitation activities can be used to generate temporal timelines. This paper is primarily aimed to introduce methods for analyzing newborn resuscitation activity timelines, through visualization, aggregation, redundancy and dimensionality reduction. We are using two datasets: 1) from Stavanger University Hospital with 108 resuscitation episodes, and 2) from Haydom Lutheran Hospital with 76 episodes. The resuscitation activity timelines were manually annotated, but in future work we will use the proposed method on automatically generated timelines from video and sensor data. We propose an encoding generator with unique codes for combination of activities. A visualization of aggregated episodes is proposed using sparse nearest neighbor graph, shown to be useful to compare datasets and give insights. Finally, we propose a method consisting of an autoencoder trained for reducing redundancy in encoded resuscitation timeline descriptions, followed by a neighborhood component analysis for dimensionality reduction. Visualization of the resulting features shows very good class separability and potential for clustering the resuscitation files according to the outcome of the newborns as dead, admitted to NICU or normal. This shows great potential for extracting important resuscitation patterns when tested on larger datasets.