Research Note: Prognostic model research: overfitting, validation and application

Can assessment of human assumed central sensitisation improve the predictive accuracy of the STarT Back screening tool in acute low back pain?

BACKGROUND

The STarT Back Screening Tool (SBT) is recommended to provide risk-stratified care in low back pain (LBP), yet its predictive value is moderate for disability and low for pain severity. Assessment of human assumed central sensitisation (HACS) in conjunction with the SBT may improve its predictive accuracy.

OBJECTIVES

To examine whether assessment of HACS in acute LBP improves the predictive accuracy of the SBT for LBP recovery at six months in people with acute non-specific LBP.

DESIGN

A prospective longitudinal study.

METHOD

Data were drawn from the UPWaRD study. One hundred and twenty people with acute non-specific LBP were recruited from the community. Baseline measures included SBT risk status, nociceptive flexor withdrawal reflex, pressure and heat pain thresholds and conditioned pain modulation. Primary outcome was the presence of LBP (pain numeric rating scale ≥1 and Roland Morris Disability Questionnaire score ≥3) at six-month follow-up. Regression coefficients were penalised using the least absolute shrinkage and selection operator technique to select predictor variables. Internal validation was performed using ten-fold cross-validation.

RESULTS/FINDINGS

SBT risk status alone did not predict the presence of LBP at six months (area under receiver operating characteristic curve [AUC] = 0.58). Adding measures of HACS to the SBT did not improve discrimination for whether LBP was present at six months (AUC = 0.59).

CONCLUSIONS

This study confirmed the suboptimal predictive accuracy of the SBT, administered during acute LBP, for LBP recovery at six months. Assessment of HACS in acute LBP does not improve the predictive accuracy of the SBT.

Wearable technology for early detection of COVID-19: A systematic scoping review

Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.

The Four-Feature Prognostic Models for Cancer-Specific and Overall Survival after Surgery for Localized Clear Cell Renal Cancer: Is There a Place for Inflammatory Markers?

We aimed at a determination of the relevance of comorbidities and selected inflammatory markers to the survival of patients with primary non-metastatic localized clear cell renal cancer (RCC). We retrospectively analyzed data from a single tertiary center on 294 patients who underwent a partial or radical nephrectomy in the years 2012–2018. The following parameters were incorporated in the risk score: tumor stage, grade, size, selected hematological markers (SIRI—systemic inflammatory response index; SII—systemic immune-inflammation index) and a comorbidities assessment tool (CCI—Charlson Comorbidity Index). For further analysis we compared our model with existing prognostic tools. In a multivariate analysis, tumor stage (p = 0.01), tumor grade (p = 0.03), tumor size (p = 0.006) and SII (p = 0.02) were significant predictors of CSS, while tumor grade (p = 0.02), CCI (p = 0.02), tumor size (p = 0.01) and SIRI (p = 0.03) were significant predictors of OS. We demonstrated that our model was characterized by higher accuracy in terms of OS prediction compared to the Leibovich and GRANT models and outperformed the GRANT model in terms of CSS prediction, while non-inferiority to the VENUSS model was revealed. Four different features were included in the predictive models for CSS (grade, size, stage and SII) and OS (grade, size, CCI and SIRI) and were characterized by adequate or even superior accuracy when compared with existing prognostic tools.