Clinical Course and Prognostic Factors of Older Patients With Back Pain and Radiating Leg Pain in General Practice: BACE Cohort Study.

OBJECTIVE: Patients with back pain (BP) and radiating leg pain have poorer clinical outcomes compared to patients with BP alone. We aimed to describe the 1-year clinical course and to identify prognostic factors associated with non-recovery in older BP patients with radiating leg pain. DESIGN: Patients in the BACE cohort aged >55 years with a new episode of BP and radiating leg pain were included (n = 377). Data on clinical outcomes were collected until 1-year follow-up. Uni- and multivariable regression analyses were performed to investigate the association between potential prognostic factors and three non-recovery outcomes at the 1-year follow-up. RESULTS: More than half of the patients (65%) did not recover after 12 months. In multivariable analyses, poor self-rated health (odds ratio [OR] 2.34, 95% CI: 1.20-4.56) and BP duration at baseline (OR 1.48, 1.12-1.96) were significantly associated with non-recovery for BP as outcome; age (OR 1.04, 1.03-1.05), smoking (OR 1.14, 1.00-1.30), depressive symptomatology (OR 1.03, 1.02-1.04), kinesiophobia (OR 1.03, 1.02-1.04), poor self-rated health (OR 2.09, 1.83-2.39), baseline disability (OR 1.16, 1.14-1.17), BP duration (OR 1.49, 1.41-1.57), leg pain (OR 1.52, 1.37-1.68), pain during rotation (OR 1.71, 1.53-1.90) and other musculoskeletal complaints (OR 1.34, 1.17-1.52) were associated with disability. No factors were associated with leg pain. CONCLUSIONS: Several prognostic factors were associated with non-recovery in older patients with BP and radiating leg pain. Primary care clinicians should be aware of these factors in managing these patients.

Diagnostic models to predict structural spinal osteoarthritis on lumbar radiographs in older adults with back pain: Development and internal validation.

Objective: It is difficult for health care providers to diagnose structural spinal osteoarthritis (OA), because current guidelines recommend against imaging in patients with back pain. Therefore, the aim of this study was to develop and internally validate multivariable diagnostic prediction models based on a set of clinical and demographic features to be used for the diagnosis of structural spinal OA on lumbar radiographs in older patients with back pain. Design: Three diagnostic prediction models, for structural spinal OA on lumbar radiographs (i.e. multilevel osteophytes, multilevel disc space narrowing (DSN), and both combined), were developed and internally validated in the 'Back Complaints in Older Adults' (BACE) cohort (N â€‹= â€‹669). Model performance (i.e. overall performance, discrimination and calibration) and clinical utility (i.e. decision curve analysis) were assessed. Internal validation was performed by bootstrapping. Results: Mean age of the cohort was 66.9 years (±7.6 years) and 59% were female. All three models included age, gender, back pain duration and duration of spinal morning stiffness as predictors. The combined model additionally included restricted lateral flexion and spinal morning stiffness severity, and exhibited the best model performance (optimism adjusted c-statistic 0.661; good calibration with intercept -0.030 and slope of 0.886) and acceptable clinical utility. The other models showed suboptimal discrimination, good calibration and acceptable decision curves. Conclusion: All three models for structural spinal OA displayed lesuboptimal discrimination and need improvement. However, these internally validated models have potential to inform primary care clinicians about a patient with risk of having structural spinal OA on lumbar radiographs. External validation before implementation in clinical care is recommended.

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

BACKGROUND: Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults. METHODS: Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively. RESULTS: We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination. CONCLUSIONS: Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

Does Adding Single-Nucleotide Polymorphisms to Risk Algorithms Improve Cardiovascular Disease Risk Prediction in Rheumatoid Arthritis? An Internal and External Validation of a Clinical Risk Score.

OBJECTIVE: Current risk algorithms do not accurately predict cardiovascular disease (CVD) risk in rheumatoid arthritis (RA). An area of interest is that of single-nucleotide polymorphisms (SNPs), of which several have been associated with CVD in the general population. We investigated whether these SNPs are associated with CVD in RA and whether SNPs could improve CVD risk prediction in RA. METHODS: Sixty SNPs were genotyped in 353 patients with RA. Logistic and Cox regression analyses were performed to identify SNPs that were associated with CVD (n = 99). A prediction model with clinical variables was made. SNPs were added to investigate the additional predictive value. Both models were internally validated. External validation was done in a separate cohort (n = 297). RESULTS: rs3184504, rs4773144, rs12190287, and rs445925 were significantly associated with new CVD. The clinical prediction model consisted of age, sex, body mass index, systolic blood pressure, high-density lipoprotein cholesterol (HDLc), and creatinine, with an area under the curve (AUC) of 0.74 (P = 0.03). Internal validation resulted in an AUC of 0.76 (P < 0.01). A new model was made including SNPs and resulted in a model with rs17011666 and rs801426, age, total cholesterol, and HDLc, which performed slightly better with an AUC of 0.77 (P < 0.01). External validation resulted in a good fit for the clinical model, but a poor fit for the SNP model. CONCLUSION: Several SNPs were associated with CVD in RA. Risk prediction slightly improved after adding SNPs to the models, but the clinical relevance is debatable. However, larger studies are needed to determine more accurately the additional value of these SNPs to CVD risk prediction algorithms.

Predicting unplanned admissions to hospital in older adults using routinely recorded general practice data: development and validation of a prediction model.

BACKGROUND: Unplanned admissions to hospital represent a hazardous event for older people. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. AIM: To develop and validate an easy-to-use prediction model for unplanned admissions to hospital in community-dwelling older adults using readily available data to allow rapid bedside assessment by GPs. DESIGN AND SETTING: This was a retrospective study using the general practice electronic health records of 243 324 community-dwelling adults aged ≥65 years linked with national administrative data to predict unplanned admissions to hospital within 6 months. METHOD: The dataset was geographically split into a development (n = 142 791/243 324, 58.7%) and validation (n = 100 533/243 324, 41.3%) sample to predict unplanned admissions to hospital within 6 months. The performance of three different models was evaluated with increasingly smaller selections of candidate predictors (optimal, readily available, and easy-to-use models). Logistic regression was used with backward selection for model development. The models were validated internally and externally. Predictive performance was assessed by area under the curve (AUC) and calibration plots. RESULTS: In both samples, 7.6% (development cohort: n = 10 839/142 791, validation cohort: n = 7675/100 533) had ≥1 unplanned hospital admission within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior admissions to hospital, pulmonary emphysema, heart failure, and polypharmacy. Its discriminative ability after validation was AUC 0.72 (95% confidence interval = 0.71 to 0.72). Calibration plots showed good calibration. CONCLUSION: The models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not have an impact on predictive performance, demonstrating the robustness of the model. An easy-to-use tool has been developed in this study that may assist GPs in decision making and with targeted preventive interventions.

Systemic lupus erythematosus is associated with an increased frequency of spontaneous preterm births: systematic review and meta-analysis.

OBJECTIVE: Preterm birth is one of the most frequent complications of pregnancy in women with systemic lupus erythematosus. The high indicated preterm birth proportion due to hypertensive disorders of pregnancy and/or fetal growth restriction is well known, and preventive measures and screening for early detection are performed. The risk of spontaneous preterm birth is less well recognized. This study aimed to determine the proportions of spontaneous and indicated preterm birth in pregnancies of women with systemic lupus erythematosus. DATA SOURCES: A systematic literature search using Pubmed, Embase, Web of Science, and Google Scholar was performed in June 2021. STUDY ELIGIBILITY CRITERIA: Studies in pregnant women with systemic lupus erythematosus reporting spontaneous and indicated preterm birth rates were selected. Original research articles published from 1995 to June 2021 were included. METHODS: Quality and risk of bias of the included studies were assessed using the Newcastle-Ottawa quality assessment scale. To estimate the pooled event rates and 95% confidence intervals, meta-analysis of single proportions with a random-effects model was performed. RESULTS: We included 21 articles, containing data of 8157 pregnancies in women with systemic lupus erythematosus. On average, 31% (95% prediction interval, 0.14-0.50) of the pregnancies resulted in preterm birth, including 14% (95% prediction interval, 0.04-0.27) spontaneous and 16% (95% prediction interval, 0.03-0.35) indicated preterm birth. CONCLUSION: In pregnant women with systemic lupus erythematosus, spontaneous and indicated preterm birth proportions are high. This information should be applied in (prepregnancy) counseling and management in pregnancy. The knowledge obtained by this meta-analysis paves the way for further research of associated risk factors and development of interventions to reduce spontaneous preterm birth in systemic lupus erythematosus pregnancies.

IDENTIFICATION OF POTENTIALLY MODIFIABLE FACTORS TO IMPROVE RECOGNITION AND OUTCOME OF NECROTIZING SOFT-TISSUE INFECTIONS.

ABSTRACT: Background : Necrotizing soft-tissue infections (NSTIs) present a surgical emergency of increasing incidence, which is often misdiagnosed and associated with substantial mortality and morbidity. A retrospective multicenter (11 hospitals) cohort study was initiated to identify the early predictors of misdiagnosis, mortality, and morbidity (skin defect size and amputation). Methods : Patients of all ages who presented with symptoms and were admitted for acute treatment of NSTIs between January 2013 and December 2017 were included. Generalized estimating equation analysis was used to identify early predictors (available before or during the first debridement surgery), with a significance level of P < 0.05. Results : The median age of the cohort (N = 216) was 59.5 (interquartile range = 23.6) years, of which 138 patients (63.9%) were male. Necrotizing soft-tissue infections most frequently originated in the legs (31.0%) and anogenital area (30.5%). More than half of the patients (n = 114, 54.3%) were initially misdiagnosed. Thirty-day mortality was 22.9%. Amputation of an extremity was performed in 26 patients (12.5%). Misdiagnosis was more likely in patients with a higher Charlson Comorbidity Index (ß = 0.20, P = 0.001), and less likely when symptoms started in the anogenital area (ß = -1.20, P = 0.003). Besides the established risk factors for mortality (septic shock and age), misdiagnosis was identified as an independent predictor of 30-day mortality (ß = 1.03, P = 0.01). The strongest predictors of the final skin defect size were septic shock (ß = 2.88, P < 0.001) and a skin-sparing approach to debridement (ß = -1.79, P = 0.002). Conclusion : Recognition of the disease is essential for the survival of patients affected by NSTI, as is adequate treatment of septic shock. The application of a skin-sparing approach to surgical debridement may decrease morbidity.

Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

BACKGROUND: Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an opportunity to analyse fall risk factor trajectories through Natural Language Processing techniques, specifically dynamic topic modelling (DTM). This study aims to uncover fall-related topics for new fallers and track their evolving trends leading up to falls. METHODS: This case-cohort study utilised primary care EHR data covering information on older adults between 2016 and 2019. Cases were individuals who fell in 2019 but had no falls in the preceding three years (2016-18). The control group was randomly sampled individuals, with similar size to the cases group, who did not endure falls during the whole study follow-up period. We applied DTM on the clinical notes collected between 2016 and 2018. We compared the trend lines of the case and control groups using the slopes, which indicate direction and steepness of the change over time. RESULTS: A total of 2,384 fallers (cases) and an equal number of controls were included. We identified 25 topics that showed significant differences in trends between the case and control groups. Topics such as medications, renal care, family caregivers, hospital admission/discharge and referral/streamlining diagnostic pathways exhibited a consistent increase in steepness over time within the cases group before the occurrence of falls. CONCLUSIONS: Early recognition of health conditions demanding care is crucial for applying proactive and comprehensive multifactorial assessments that address underlying causes, ultimately reducing falls and fall-related injuries.

Development and Internal Validation of a Novel Nomogram Predicting the Outcome of Salvage Radiation Therapy for Biochemical Recurrence after Radical Prostatectomy in Patients without Metastases on Restaging Prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomography.

Background and objective: Owing to the greater use of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) in patients with biochemical recurrence (BCR) of prostate cancer (PCa) after robot-assisted radical prostatectomy (RARP), patient selection for local salvage radiation therapy (sRT) has changed. Our objective was to determine the short-term efficacy of sRT in patients with BCR after RARP, and to develop a novel nomogram predicting BCR-free survival after sRT in a nationwide contemporary cohort of patients who underwent PSMA PET/CT before sRT for BCR of PCa, without evidence of metastatic disease. Methods: All 302 eligible patients undergoing PCa sRT in four reference centers between September 2015 and August 2020 were included. We conducted multivariable logistic regression analysis using a backward elimination procedure to develop a nomogram for predicting biochemical progression of PCa, defined as prostate-specific antigen (PSA) ≥0.2 ng/ml above the post-sRT nadir within 1 yr after sRT. Key findings and limitations: Biochemical progression of disease within 1 yr after sRT was observed for 56/302 (19%) of the study patients. The final predictive model included PSA at sRT initiation, pathological grade group, surgical margin status, PSA doubling time, presence of local recurrence on PSMA PET/CT, and the presence of biochemical persistence (first PSA result ≥0.1 ng/ml) after RARP. The area under the receiver operating characteristic curve for this model was 0.72 (95% confidence interval 0.64-0.79). Using our nomogram, patients with a predicted risk of >20% had a 30.8% chance of developing biochemical progression within 1 yr after sRT. Conclusions: Our novel nomogram may facilitate better patient counseling regarding early oncological outcome after sRT. Patients with high risk of biochemical progression may be candidates for more extensive treatment. Patient summary: We developed a new tool for predicting cancer control outcomes of radiotherapy for patients with recurrence of prostate cancer after surgical removal of their prostate. This tool may help in better counseling of these patients with recurrent cancer regarding their early expected outcome after radiotherapy.

Trajectories of Adherence to Home-Based Exercise Recommendations Among People With Low Back Pain: A Longitudinal Analysis.

OBJECTIVE: This study aimed to examine the presence of distinct trajectories of adherence to home-based exercise recommendations among people with low back pain (LBP). This study also aimed to identify differences in baseline characteristics among groups. METHODS: This study was a secondary analysis of a prospective, multicenter cluster randomized controlled trial investigating the cost-effectiveness of a stratified blended physical therapist intervention compared to usual care physical therapy in patients with LBP. The intervention group received usual care with integrated support via a smartphone app. A total of 208 patients were recruited from 58 primary care physical therapist practices. Baseline data included patient characteristics, physical functioning, pain intensity, physical activity, fear avoidance, pain catastrophizing, self-efficacy, self-management ability, and health-related quality of life. The Exercise Adherence Scale (score range = 0-100) was used to measure adherence during each treatment session. Latent class growth analysis was used to estimate trajectories of adherence. RESULTS: Adherence data were available from 173 out of 208 patients (83%). Data were collected during an average of 5.1 (standard deviation [SD] = 2.5) treatment sessions, with total treatment duration of 51 (SD = 41.7) days. Three trajectory classes were identified: "declining adherence" (12%), "stable adherence" (45%), and "increasing adherence" (43%). No differences in baseline characteristic were found between groups. CONCLUSION: Three adherence trajectories to exercise recommendations were identified in patients with LBP. However, baseline characteristics cannot identify a patient's trajectory group. IMPACT: Despite the presence of distinct trajectories of adherence in patients with LBP, physical therapists should not attempt to place a patient in a trajectory group at the start of treatment. Instead, adherence should be closely monitored as treatment progresses and supported when required as part of an ongoing process.

Prognostic model using 18F-FDG PET radiomics predicts progression-free survival in relapsed/refractory Hodgkin lymphoma.

Investigating prognostic factors in patients with relapsed or primary refractory classical Hodgkin lymphoma (R/R cHL) is essential to optimize risk-adapted treatment strategies. We built a prognostic model using baseline quantitative 18F-fluorodeoxyglucose positron emission tomography (PET) radiomics features and clinical characteristics to predict the progression-free survival (PFS) among patients with R/R cHL treated with salvage chemotherapy followed by autologous stem cell transplantation. Metabolic tumor volume and several novel radiomics dissemination features, representing interlesional differences in distance, volume, and standard uptake value, were extracted from the baseline PET. Machine learning using backward selection and logistic regression were applied to develop and train the model on a total of 113 patients from 2 clinical trials. The model was validated on an independent external cohort of 69 patients. In addition, we validated 4 different PET segmentation methods to calculate radiomics features. We identified a subset of patients at high risk for progression with significant inferior 3-year PFS outcomes of 38.1% vs 88.4% for patients in the low-risk group in the training cohort (P < .001) and 38.5% vs 75.0% in the validation cohort (P = .015), respectively. The overall survival was also significantly better in the low-risk group (P = .022 and P < .001). We provide a formula to calculate a risk score for individual patients based on the model. In conclusion, we developed a prognostic model for PFS combining radiomics and clinical features in a large cohort of patients with R/R cHL. This model calculates a PET-based risk profile and can be applied to develop risk-stratified treatment strategies for patients with R/R cHL. These trials were registered at www.clinicaltrials.gov as #NCT02280993, #NCT00255723, and #NCT01508312.

Sensitivity of an AI method for [18F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols.

BACKGROUND: Convolutional neural networks (CNNs), applied to baseline [18F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to different image reconstruction protocols. Baseline [18F]FDG PET/CT scans were collected from 20 DLBCL patients. EARL1, EARL2 and high-resolution (HR) protocols were applied per scan, generating three images with different image qualities. Image-based transformation was applied by blurring EARL2 and HR images to generate EARL1 compliant images using a Gaussian filter of 5 and 7 mm, respectively. MIPs were generated for each of the reconstructions, before and after image transformation. An in-house developed CNN predicted the probability of tumor progression within 2 years for each MIP. The difference in probabilities per patient was then calculated between both EARL2 and HR with respect to EARL1 (delta probabilities or ΔP). We compared these to the probabilities obtained after aligning the data with ComBat using the difference in median and interquartile range (IQR). RESULTS: CNN probabilities were found to be sensitive to different reconstruction protocols (EARL2 ΔP: median = 0.09, interquartile range (IQR) = [0.06, 0.10] and HR ΔP: median = 0.1, IQR = [0.08, 0.16]). Moreover, higher resolution images (EARL2 and HR) led to higher probability values. After image-based and ComBat transformation, an improved agreement of CNN probabilities among reconstructions was found for all patients. This agreement was slightly better after image-based transformation (transformed EARL2 ΔP: median = 0.022, IQR = [0.01, 0.02] and transformed HR ΔP: median = 0.029, IQR = [0.01, 0.03]). CONCLUSION: Our CNN-based outcome predictions are affected by the applied reconstruction protocols, yet in a predictable manner. Image-based harmonization is a suitable approach to harmonize CNN predictions across image reconstruction protocols.

Validation of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Geriatric Outpatients.

OBJECTIVES: Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE_IT models for predicting any fall and recurrent falls, referred as Any_fall and Recur_fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone. DESIGN: Retrospective, combined analysis of 2 prospective cohorts. SETTING AND PARTICIPANTS: Data were included of 1125 patients (aged ≥65 years) who visited the geriatrics department or the emergency department. METHODS: We evaluated the models' discrimination using the C-statistic. Models were updated using logistic regression if calibration intercept or slope values deviated significantly from their ideal values. Decision curve analysis was applied to compare the models' clinical value (ie, net benefit) against that of falls history for different decision thresholds. RESULTS: During the 1-year follow-up, 428 participants (42.7%) endured 1 or more falls, and 224 participants (23.1%) endured a recurrent fall (≥2 falls). C-statistic values were 0.66 (95% CI 0.63-0.69) and 0.69 (95% CI 0.65-0.72) for the Any_fall and Recur_fall models, respectively. Any_fall overestimated the fall risk and we therefore updated only its intercept whereas Recur_fall showed good calibration and required no update. Compared with falls history, Any_fall and Recur_fall showed greater net benefit for decision thresholds of 35% to 60% and 15% to 45%, respectively. CONCLUSIONS AND IMPLICATIONS: The models performed similarly in this data set of geriatric outpatients as in the development sample. This suggests that fall-risk assessment tools that were developed in community-dwelling older adults may perform well in geriatric outpatients. We found that in geriatric outpatients the models have greater clinical value across a wide range of decision thresholds compared with screening for falls history alone.

Predictors of poor outcome following lumbar spinal fusion surgery: a prospective observational study to derive two clinical prediction rules using British Spine Registry data.

PURPOSE: Lumbar spinal fusion surgery (LSFS) is common for lumbar degenerative disorders. The objective was to develop clinical prediction rules to identify which patients are likely to have a favourable outcome to inform decisions regarding surgery and rehabilitation. METHODS: A prospective observational study recruited 600 (derivation) and 600 (internal validation) consecutive adult patients undergoing LSFS for degenerative lumbar disorder through the British Spine Registry. Definition of good outcome (6 weeks, 12 months) was reduction in pain intensity (Numerical Rating Scale, 0-10) and disability (Oswestry Disability Index, ODI 0-50) > 1.7 and 14.3, respectively. Linear and logistic regression models were fitted and regression coefficients, Odds ratios and 95% CIs reported. RESULTS: Lower BMI, higher ODI and higher leg pain pre-operatively were predictive of good disability outcome, higher back pain was predictive of good back pain outcome, and no previous surgery and higher leg pain were predictive of good leg pain outcome; all at 6 weeks. Working and higher leg pain were predictive of good ODI and leg pain outcomes, higher back pain was predictive of good back pain outcome, and higher leg pain was predictive of good leg pain outcome at 12 months. Model performance demonstrated reasonable to good calibration and adequate/very good discrimination. CONCLUSIONS: BMI, ODI, leg and back pain and previous surgery are important considerations pre-operatively to inform decisions for surgery. Pre-operative leg and back pain and work status are important considerations to inform decisions for management following surgery. Findings may inform clinical decision making regarding LSFS and associated rehabilitation.

Development and External Validation of a Novel Nomogram to Predict the Probability of Pelvic Lymph-node Metastases in Prostate Cancer Patients Using Magnetic Resonance Imaging and Molecular Imaging with Prostate-specific Membrane Antigen Positron Emission Tomography.

BACKGROUND: Preoperative assessment of the probability of pelvic lymph-node metastatic disease (pN1) is required to identify patients with prostate cancer (PCa) who are candidates for extended pelvic lymph-node dissection (ePLND). OBJECTIVE: To develop a novel intuitive prognostic nomogram for predicting pathological lymph-node (pN) status in contemporary patients with primary diagnosed localized PCa, using preoperative clinical and histopathological parameters, magnetic resonance imaging (MRI), and prostate-specific membrane antigen (PSMA) positron emission tomography (PET). DESIGN, SETTING, AND PARTICIPANTS: In total, 700 eligible patients who underwent robot-assisted radical prostatectomy and ePLND were included in the model-building cohort. The external validation cohort consisted of 305 surgically treated patients. Logistic regression with backward elimination was used to select variables for the Amsterdam-Brisbane-Sydney nomogram. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Performance of the final model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision-curve analyses. Models were subsequently validated in an external population. RESULTS AND LIMITATIONS: The Amsterdam-Brisbane-Sydney nomogram included initial prostate-specific antigen value, MRI T stage, highest biopsy grade group (GG), biopsy technique, percentage of systematic cores with clinically significant PCa (GG ≥2), and lymph-node status on PSMA-PET. The AUC for predicting pN status was 0.81 (95% confidence interval [CI] 0.78-0.85) for the final model. On external validation, the Amsterdam-Brisbane-Sydney nomogram showed superior discriminative ability to the Briganti-2017 and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms (AUC 0.75 [95% CI 0.69-0.81] vs 0.67 [95% CI 0.61-0.74] and 0.65 [95% CI 0.58-0.72], respectively; p < 0.05), and similar discriminative ability to the Briganti-2019 nomogram (AUC 0.78 [95% CI 0.71-0.86] vs 0.80 [95% CI 0.73-0.86]; p = 0.76). The Amsterdam-Brisbane-Sydney nomogram showed excellent calibration on external validation, with an increased net benefit at a threshold probability of ≥4%. CONCLUSIONS: The validated Amsterdam-Brisbane-Sydney nomogram performs superior to the Briganti-2017 and MSKCC nomograms, and similar to the Briganti-2019 nomogram. Furthermore, it is applicable in all patients with newly diagnosed unfavorable intermediate- and high-risk PCa. PATIENT SUMMARY: We developed and validated the Amsterdam-Brisbane-Sydney nomogram for the prediction of prostate cancer spread to lymph nodes before surgery. This nomogram performs similar or superior to all presently available nomograms.

Predicting future falls in older people using natural language processing of general practitioners' clinical notes.

BACKGROUND: Falls in older people are common and morbid. Prediction models can help identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity to develop automated prediction tools that may help to identify fall-prone individuals and lower clinical workload. However, existing models primarily utilise structured EHR data and neglect information in unstructured data. Using machine learning and natural language processing (NLP), we aimed to examine the predictive performance provided by unstructured clinical notes, and their incremental performance over structured data to predict falls. METHODS: We used primary care EHR data of people aged 65 or over. We developed three logistic regression models using the least absolute shrinkage and selection operator: one using structured clinical variables (Baseline), one with topics extracted from unstructured clinical notes (Topic-based) and one by adding clinical variables to the extracted topics (Combi). Model performance was assessed in terms of discrimination using the area under the receiver operating characteristic curve (AUC), and calibration by calibration plots. We used 10-fold cross-validation to validate the approach. RESULTS: Data of 35,357 individuals were analysed, of which 4,734 experienced falls. Our NLP topic modelling technique discovered 151 topics from the unstructured clinical notes. AUCs and 95% confidence intervals of the Baseline, Topic-based and Combi models were 0.709 (0.700-0.719), 0.685 (0.676-0.694) and 0.718 (0.708-0.727), respectively. All the models showed good calibration. CONCLUSIONS: Unstructured clinical notes are an additional viable data source to develop and improve prediction models for falls compared to traditional prediction models, but the clinical relevance remains limited.

Urinary volatile organic compounds for colorectal cancer screening: A systematic review and meta-analysis.

BACKGROUND: The faecal immunochemical test (FIT) suffers from suboptimal performance and participation in colorectal cancer (CRC) screening. Urinary volatile organic compounds (VOCs) may be a useful alternative. We aimed to determine the diagnostic potential of urinary VOCs for CRC/adenomas. By relating VOCs to known pathways, we aimed to gain insight into the pathophysiology of colorectal neoplasia. METHODS: A systematic search was performed in PubMed, EMBASE and Web of Science. Original studies on urinary VOCs for CRC/adenoma detection with a control group were included. QUADAS-2 tool was used for quality assessment. Meta-analysis was performed by adopting a bivariate model for sensitivity/specificity. Fagan's nomogram estimated the performance of combined FIT-VOC. Neoplasm-associated VOCs were linked to pathways using the KEGG database. RESULTS: Sixteen studies-involving 837 CRC patients and 1618 controls-were included; 11 performed chemical identification and 7 chemical fingerprinting. In all studies, urinary VOCs discriminated CRC from controls. Pooled sensitivity and specificity for CRC based on chemical fingerprinting were 84% (95% CI 73-91%) and 70% (95% CI 63-77%), respectively. The most distinctive individual VOC was butanal (AUC 0.98). The estimated probability of having CRC following negative FIT was 0.38%, whereas 0.09% following negative FIT-VOC. Combined FIT-VOC would detect 33% more CRCs. In total 100 CRC-associated urinary VOCs were identified; particularly hydrocarbons, carboxylic acids, aldehydes/ketones and amino acids, and predominantly involved in TCA-cycle or alanine/aspartate/glutamine/glutamate/phenylalanine/tyrosine/tryptophan metabolism, which is supported by previous research on (colorectal)cancer biology. The potential of urinary VOCs to detect precancerous adenomas or gain insight into their pathophysiology appeared understudied. CONCLUSION: Urinary VOCs hold potential for non-invasive CRC screening. Multicentre validation studies are needed, especially focusing on adenoma detection. Urinary VOCs elucidate underlying pathophysiologic processes.

Discussing depression in patients with visual impairment differs across countries: Validation of a prediction model in healthcare providers.

PURPOSE: Healthcare providers often experience difficulties in discussing depression with adults with visual impairment (VI), obstructing timely referral. The purpose of this study was to examine predictors of routine discussions of depression with adults with VI from the perspective of different healthcare providers from different countries. METHODS: Cross-sectional survey data from Welsh (N = 122), Australian (N = 94) and Dutch (N = 100) healthcare providers, that is eye care practitioners (ECPs) and low-vision care providers (LVCPs), were analysed. Multivariable logistic regression analysis was performed in the Welsh sample to determine predictors for discussing depression. Internal validation was conducted by using a bootstrap method, and the recalibrated model was externally validated in the Australian and Dutch sample. RESULTS: Work experience in eye care services (OR 0.95; 95% confidence interval (CI) 0.92 to 0.99) and perceived barriers (OR 0.95; 95% CI 0.92 to 0.98) was found to predict discussing depression with patients. The area under the curve (AUC) of 0.73 reflected good discrimination of the model. The model showed a slightly better fit in the Australian sample (AUC = 0.77), but a poor fit in the Dutch sample. CONCLUSION: The final prediction model was not generalizable to Dutch healthcare providers. They perceived less barriers in depression management than Welsh and Australian healthcare providers. This could be explained by differences in ECPs and LVCPs roles and responsibilities, increased attention on mental health and differences in organizing health care. Differences between healthcare providers' responsibilities and support needs should be taken into account while creating a facilitating environment to discuss depression.

External validation and updating of prognostic models for predicting recovery of disability in people with (sub)acute neck pain was successful: broad external validation in a new prospective cohort.

QUESTION: Can existing post-treatment prognostic models for predicting neck pain recovery (primarily in terms of disability and secondarily in terms of pain intensity and perceived improvement) be externally validated and updated at the end of the treatment period and at 6 and 12 weeks of follow-up in a new Dutch cohort of people with neck pain treated with guideline-based usual care physiotherapy? DESIGN: External validation and model updating in a new prospective cohort of three previously developed prognostic models. PARTICIPANTS: People with (sub)acute neck pain and registered for primary care physiotherapy treatment. OUTCOME MEASURES: Recovery of disability, pain intensity, and perceived recovery at 6 and 12 weeks and at the end of the treatment period. RESULTS: Discriminative performance (c-statistic) of the disability model at 6 weeks was 0.73 (95% CI 0.69 to 0.77) and reasonably well calibrated after intercept recalibration. The disability model at 12 weeks and at the end of the treatment period showed discriminative c-statistic performance values of 0.69 (95% CI 0.64 to 0.73) and 0.68 (95% CI 0.63 to 0.72), respectively, and was well calibrated. Pain models and perceived recovery models did not reach acceptable performance. Cervical mobility added value to the disability models and pain catastrophising to the disability and pain models at 6 weeks. DISCUSSION: Broad external validation of the disability model was successful in people with (sub)acute neck pain and clinicians may use this model in clinical practice with reasonable accuracy. Further research is required to assess the disability model's clinical impact and generalisability, and to identify additional valuable model predictors. REGISTRATION: https://osf.io/a6r3k/.

Development and validation of an intrinsic capacity composite score in the Longitudinal Aging Study Amsterdam: a formative approach.

BACKGROUND: Intrinsic capacity (IC) defined by the WHO refers to the composite of five domains of capacities. So far, developing and validating a standardized overall score of the concept have been challenging partly because its conceptual framework has been unclear. We consider that a person's IC is determined by its domain-specific indicators suggesting a formative measurement model. AIMS: To develop an IC score applying a formative approach and assess its validity. METHODS: The study sample (n = 1908) consisted of 57-88-year-old participants from the Longitudinal Aging Study Amsterdam (LASA). We used logistic regression models to select the indicators to the IC score with 6-year functional decline as an outcome. An IC score (range 0-100) was constructed for each participant. We examined the known-groups' validity of the IC score by comparing groups based on age and number of chronic diseases. The criterion validity of the IC score was assessed with 6-year functional decline and 10-year mortality as outcomes. RESULTS: The constructed IC score included seven indicators covering all five domains of the construct. The mean IC score was 66.7 (SD 10.3). The scores were higher among younger participants and those who had lower number of chronic diseases. After adjustment for sociodemographic indicators, chronic diseases, and BMI, a one-point higher IC score was associated with a 7% decreased risk for 6-year functional decline and a 2% decreased risk for 10-year mortality. CONCLUSIONS: The developed IC score demonstrated discriminative ability according to age and health status and is associated with subsequent functional decline and mortality.