Development and validation of claims-based algorithms for estimating gestational age of spontaneous abortion and termination.

To study the risk of spontaneous abortion (SAB) or termination using healthcare utilization databases, algorithms to estimate the gestational age (GA) are needed. Using Medicaid data, we developed a hierarchical algorithm to classify pregnancy outcomes. We identified the subset of potential SAB and termination cases, and abstracted the GA from linked electronic medical records (gold standard). We developed three approaches: (1) assign median GA for SAB and termination cases in the US; (2) draw a random GA from the population distributions; (3) estimate GA based on regression models. Algorithm performance was assessed based on the proportion of pregnancies with estimated GA within 1-4 weeks of the gold standard, the mean squared error (MSE) and the R-squared. Approach 1 and Approach 3 had similar performance, though approach 3 using random forest models with variables selected via the Boruta algorithm had better MSE and R-squared. For SAB, 58.0% of pregnancies were correctly classified within 2 weeks of the gold standard (MSE: 8.7, R-squared: 0.09). For termination, the proportions were 66.3% (MSE: 11.7; R-squared: 0.35). SABs and terminations can be studied in healthcare utilization data with careful implementation of validated algorithms though higher level of GA misclassification is expected compared to live births.

External validation of the Dat'AIDS score: A risk score for predicting 5-year overall mortality in people living with HIV aged 60 years or older.

OBJECTIVE: To perform an external validation of the Dat'AIDS score for predicting 5-year overall mortality among people with HIV (PWH) aged 60 years or older. METHODS: This was a multi-centre prospective cohort study at all sites participating in the Swiss HIV Cohort Study (SHCS). We calculated the Dat'AIDS score in PWH aged 60 years or older at their first visit between 1 January 2015 and 1 January 2020. People living with HIV-2 and those whose Dat'AIDS score could not be calculated were excluded. Patients were followed until 1 January 2020. The primary endpoint was all-cause mortality. Vital status was collected throughout the study period. We obtained population and score descriptive statistics and assessed the score's discrimination and calibration. RESULTS: We included 2205 participants (82% male) of median [interquartile range (IQR)] age 62.0 (60.3-67.0) years, mostly with viraemia <50 copies/mL (92.7%). Median follow-up time was 15.9 years and median (IQR) CD4 cell count at enrolment was 586 (420-782) cells/µL. In all, 152 deaths were recorded during a total follow-up period of 7147 patient-years. The median (IQR) observed Dat'AIDS score was 3 (0-8). Discriminative capacities were good as the C-statistic was 0.73 (95% CI: 0.69-0.77) and consistent across all subgroups. Comparison of observed and expected survival probabilities showed good calibration. CONCLUSIONS: External validation of the Dat'AIDS score in patients aged 60 years or older showed that it could be a useful tool not only for research purposes, but also to identify older patients at a higher mortality risk and to tailor the most appropriate interventions.

Predictive Validity of the Academy of Nutrition and Dietetics/American Society for Parental Nutrition Indicators to Diagnose Malnutrition and the Screening Tool for Risk on Nutritional Status and Growth among Hospitalized Children Relative to Medical Outcomes.

OBJECTIVE: To evaluate predictive validity of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition Indicators to diagnose pediatric malnutrition (AAIMp) and the Screening Tool for Risk on Nutritional Status and Growth (STRONGkids) in regard to pediatric patient outcomes in US hospitals. STUDY DESIGN: A prospective cohort study (Clinical Trial Registry: NCT03928548) was completed from August 2019 through January 2023 with 27 pediatric hospitals or units from 18 US states and Washington DC. RESULTS: Three hundred and forty-five children were enrolled in the cohort (n = 188 in the AAIMp validation subgroup). There were no significant differences in the incidence of emergency department visits and hospital readmissions, hospital length of stay (LOS), or health care resource utilization for children diagnosed with mild, moderate, or severe malnutrition using the AAIMp tool compared with children with no malnutrition diagnosis. The STRONGkids tool significantly predicted more emergency department visits and hospital readmissions for children at moderate and high malnutrition risk (moderate risk - incidence rate ratio 1.65, 95% CI: 1.09, 2.49, P = .018; high risk - incidence rate ratio 1.64, 95% CI: 1.05, 2.56, P = .028) and longer LOS (43.8% longer LOS, 95% CI: 5.2%, 96.6%, P = .023) for children at high risk compared with children at low risk after adjusting for patient characteristics. CONCLUSIONS: Malnutrition risk based on the STRONGkids tool predicted poor medical outcomes in hospitalized US children; the same relationship was not observed for a malnutrition diagnosis based on the AAIMp tool.

Development of a clinical algorithm to predict phenotypic switches between atopic dermatitis and psoriasis (the "Flip-Flop" phenomenon).

BACKGROUND: Atopic dermatitis (AD) and psoriasis vulgaris (PV) are almost mutually exclusive diseases with different immune polarizations, mechanisms and therapeutic targets. Switches to the other disease ("Flip-Flop" [FF] phenomenon) can occur with or without systemic treatment and are often referred to as paradoxical reactions under biological therapy. METHODS: The objective was to develop a diagnostic algorithm by combining clinical criteria of AD and PV to identify FF patients. The algorithm was prospectively validated in patients enrolled in the CK-CARE registry in Bonn, Germany. Afterward, algorithm refinements were implemented based on machine learning. RESULTS: Three hundred adult Caucasian patients were included in the validation study (n = 238 with AD, n = 49 with PV, n = 13 with FF; mean age 41.2 years; n = 161 [53.7%] female). The total FF scores of the PV and AD groups differed significantly from the FF group in the validation data (p < .001). The predictive mean generalized Youden-Index of the initial model was 78.9% [95% confidence interval 72.0%-85.6%] and the accuracy was 89.7%. Disease group-specific sensitivity was 100% (FF), 95.0% (AD), and 61.2% (PV). The specificity was 89.2% (FF), 100% (AD), and 100% (PV), respectively. CONCLUSION: The FF algorithm represents the first validated tool to identify FF patients.

Psychometric Validation of the Modified Functional Scale for the Assessment and Rating of Ataxia (f-SARA) in Patients With Spinocerebellar Ataxia.

This study aimed to generate evidence to support psychometric validity of the modified functional Scale for the Assessment and Rating of Ataxia (f-SARA) among patients with spinocerebellar ataxia (SCA). Psychometric measurement properties and minimal change thresholds of the f-SARA were evaluated using data from a cohort of SCA subjects (recruited at Massachusetts General Hospital [MGH]; n = 33) and data from a phase 3 trial of troriluzole in adults with SCA (NCT03701399 [Study 206]; n = 217), including a subset of patients with the SCA3 genotype (n = 89). f-SARA item ceiling effects were absent within the MGH cohort, while floor effects were present. Excellent internal consistency reliability was demonstrated (αtotal = 0.90; αitems-removed = 0.86-0.90), and item-to-total correlations were strong (r = 0.82-0.91, per item). High test-retest reliability was demonstrated with intraclass correlation coefficients of 0.91 (total) and 0.73-0.92 (items). Convergent and divergent validity was supported, with strong correlations observed between the f-SARA and similarly constructed scales (FARS-FUNC, BARS, PROM-ADL, and FARS-ADL; all p < 0.001) and weaker correlations observed among measures of differing constructs. Mean item and total scores increased with disease severity (by FARS-FUNC quartile; p < 0.001). A 1-point threshold for meaningful changes was supported as 0.5 × SD = 0.89, SEM = 1.12, and mean changes from baseline for patients classified as "improved," "no change," or "deteriorated" were -0.68, 0.02, and 0.58, respectively. Similar trends were observed in Study 206 all-SCA and SCA3 cohorts. The measurement properties of the f-SARA provide evidence of its psychometric validity, responsiveness, and suitability as a clinical outcome measure in patients with SCA, including those with SCA3.

Cut-Offs for Disease Activity States in Axial Spondyloarthritis With Ankylosing Spondylitis Disease Activity Score (ASDAS) Based on C-Reactive Protein and ASDAS Based on Erythrocyte Sedimentation Rate: Are They Interchangeable?

OBJECTIVE: Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP) is recommended over ASDAS based on erythrocyte sedimentation rate (ASDAS-ESR) to assess disease activity in axial spondyloarthritis (axSpA). Although ASDAS-CRP and ASDAS-ESR are not interchangeable, the same disease activity cut-offs are used for both. We aimed to estimate optimal ASDAS-ESR values corresponding to the established ASDAS-CRP cut-offs (1.3, 2.1, and 3.5) and investigate the potential improvement of level of agreement between ASDAS-ESR and ASDAS-CRP disease activity states when applying these estimated cut-offs. METHODS: We used data from patients with axSpA from 9 European registries initiating a tumor necrosis factor inhibitor. ASDAS-ESR cut-offs were estimated using the Youden index. The level of agreement between ASDAS-ESR and ASDAS-CRP disease activity states was compared against each other. RESULTS: In 3664 patients, mean ASDAS-CRP was higher than ASDAS-ESR at both baseline (3.6 and 3.4, respectively) and aggregated follow-up at 6, 12, or 24 months (1.9 and 1.8, respectively). The estimated ASDAS-ESR values corresponding to the established ASDAS-CRP cut-offs were 1.4, 1.9, and 3.3. By applying these cut-offs, the proportion of discordance between disease activity states according to ASDAS-ESR and ASDAS-CRP decreased from 22.93% to 19.81% in baseline data but increased from 27.17% to 28.94% in follow-up data. CONCLUSION: We estimated the optimal ASDAS-ESR values corresponding to the established ASDAS-CRP cut-off values. However, applying the estimated cut-offs did not increase the level of agreement between ASDAS-ESR and ASDAS-CRP disease activity states to a relevant degree. Our findings did not provide evidence to reject the established cut-off values for ASDAS-ESR.

The Finger Dexterity Test: Validation study of a smartphone-based manual dexterity assessment.

BACKGROUND: The Nine-Hole Peg Test (9HPT) is the golden standard to measure manual dexterity in people with multiple sclerosis (MS). However, administration requires trained personnel and dedicated time during a clinical visit. OBJECTIVES: The objective of this study is to validate a smartphone-based test for remote manual dexterity assessment, the icompanion Finger Dexterity Test (FDT), to be included into the icompanion application. METHODS: A total of 65 MS and 81 healthy subjects were tested, and 20 healthy subjects were retested 2 weeks later. RESULTS: The FDT significantly correlated with the 9HPT (dominant: ρ = 0.62, p < 0.001; non-dominant: ρ = 0.52, p < 0.001). MS subjects had significantly higher FDT scores than healthy subjects (dominant: p = 0.015; non-dominant: p = 0.013), which was not the case for the 9HPT. A significant correlation with age (dominant: ρ = 0.46, p < 0.001; non-dominant: ρ = 0.40, p = 0.002), Expanded Disability Status Scale (EDSS, dominant: ρ = 0.36, p = 0.005; non-dominant: ρ = 0.31, p = 0.024), and disease duration for the non-dominant hand (ρ = 0.31, p = 0.016) was observed. There was a good test-retest reliability in healthy subjects (dominant: r = 0.69, p = 0.001; non-dominant: r = 0.87, p < 0.001). CONCLUSIONS: The icompanion FDT shows a moderate-to-good concurrent validity and test-retest reliability, differentiates between the MS subjects and healthy controls, and correlates with clinical parameters. This test can be implemented into routine MS care for remote follow-up of manual dexterity.

Evaluation of left ventricular and left atrial volumetric function from native MR multislice 4D flow magnitude data.

OBJECTIVES: To assess the feasibility, precision, and accuracy of left ventricular (LV) and left atrial (LA) volumetric function evaluation from native magnetic resonance (MR) multislice 4D flow magnitude images. MATERIALS & METHODS: In this prospective study, 60 subjects without signs or symptoms of heart failure underwent 3T native cardiac MR multislice 4D flow and bSSFP-cine realtime imaging. LV and LA volumetric function parameters were evaluated from 4D flow magnitude (4D flow-cine) and bSSFP-cine data using standard software to obtain end-diastolic volume (EDV), end-systolic volume (ESV), ejection-fraction (EF), stroke-volume (SV), LV muscle mass (LVM), LA maximum volume, LA minimum volume, and LA total ejection fraction (LATEF). Stroke volumes derived from both imaging methods were further compared to 4D pulmonary artery flow-derived net forward volumes (NFV). Methods were compared by correlation and Bland-Altman analysis. RESULTS: Volumetric function parameters from 4D flow-cine and bSSFP-cine showed high to very high correlations (r = 0.83-0.98). SV, LA volumes and LATEF did not differ between methods. LV end-diastolic and end-systolic volumes were slightly underestimated (EDV: -2.9 ± 5.8 mL; ESV: -2.3 ± 3.8 mL), EF was slightly overestimated (EF: 0.9 ± 2.6%), and LV mass was considerably overestimated (LVM: 39.0 ± 11.4 g) by 4D flow-cine imaging. SVs from both methods correlated very highly with NFV (r = 0.91 in both cases) and did not differ from NFV. CONCLUSION: Native multislice 4D flow magnitude data allows precise evaluation of LV and LA volumetric parameters; however, apart from SV, LV volumetric parameters demonstrate bias and need to be referred to their respective normal values. CLINICAL RELEVANCE STATEMENT: Volumetric function assessment from native multislice 4D flow magnitude images can be performed with routinely used clinical software, facilitating the application of 4D flow as a one-stop-shop functional cardiac MR exam, providing consistent, simultaneously acquired, volume and flow data. KEY POINTS: • Native multislice 4D flow imaging allows evaluation of volumetric left ventricular and atrial function parameters. • Left ventricular and left atrial function parameters derived from native multislice 4D flow data correlate highly with corresponding standard cine-derived parameters. • Multislice 4D flow-derived volumetric stroke volume and net forward volume do not differ.

Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans.

OBJECTIVES: This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans. METHODS: This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed. RESULTS: A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively. CONCLUSIONS: Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern. CLINICAL RELEVANCE STATEMENT: Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules. KEY POINTS: Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required.

A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year.

Age estimations are relevant for pre-trial detention, sentencing in criminal cases and as part of the evaluation in asylum processes to protect the rights and privileges of minors. No current method can determine an exact chronological age due to individual variations in biological development. This study seeks to develop a validated statistical model for estimating an age relative to key legal thresholds (15, 18, and 21 years) based on a skeletal (CT-clavicle, radiography-hand/wrist or MR-knee) and tooth (radiography-third molar) developmental stages. The whole model is based on 34 scientific studies, divided into examinations of the hand/wrist (15 studies), clavicle (5 studies), distal femur (4 studies), and third molars (10 studies). In total, data from approximately 27,000 individuals have been incorporated and the model has subsequently been validated with data from 5,000 individuals. The core framework of the model is built upon transition analysis and is further developed by a combination of a type of parametric bootstrapping and Bayesian theory. Validation of the model includes testing the models on independent datasets of individuals with known ages and shows a high precision with separate populations aligning closely with the model's predictions. The practical use of the complex statistical model requires a user-friendly tool to provide probabilities together with the margin of error. The assessment based on the model forms the medical component for the overall evaluation of an individual's age.

Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage.

BACKGROUND: Prediction models are often externally validated with data from a single study or cohort. However, the interpretation of performance estimates obtained with single-study external validation is not as straightforward as assumed. We aimed to illustrate this by conducting a large number of external validations of a prediction model for functional outcome in subarachnoid hemorrhage (SAH) patients. METHODS: We used data from the Subarachnoid Hemorrhage International Trialists (SAHIT) data repository (n = 11,931, 14 studies) to refit the SAHIT model for predicting a dichotomous functional outcome (favorable versus unfavorable), with the (extended) Glasgow Outcome Scale or modified Rankin Scale score, at a minimum of three months after discharge. We performed leave-one-cluster-out cross-validation to mimic the process of multiple single-study external validations. Each study represented one cluster. In each of these validations, we assessed discrimination with Harrell's c-statistic and calibration with calibration plots, the intercepts, and the slopes. We used random effects meta-analysis to obtain the (reference) mean performance estimates and between-study heterogeneity (I2-statistic). The influence of case-mix variation on discriminative performance was assessed with the model-based c-statistic and we fitted a "membership model" to obtain a gross estimate of transportability. RESULTS: Across 14 single-study external validations, model performance was highly variable. The mean c-statistic was 0.74 (95%CI 0.70-0.78, range 0.52-0.84, I2 = 0.92), the mean intercept was -0.06 (95%CI -0.37-0.24, range -1.40-0.75, I2 = 0.97), and the mean slope was 0.96 (95%CI 0.78-1.13, range 0.53-1.31, I2 = 0.90). The decrease in discriminative performance was attributable to case-mix variation, between-study heterogeneity, or a combination of both. Incidentally, we observed poor generalizability or transportability of the model. CONCLUSIONS: We demonstrate two potential pitfalls in the interpretation of model performance with single-study external validation. With single-study external validation. (1) model performance is highly variable and depends on the choice of validation data and (2) no insight is provided into generalizability or transportability of the model that is needed to guide local implementation. As such, a single single-study external validation can easily be misinterpreted and lead to a false appreciation of the clinical prediction model. Cross-validation is better equipped to address these pitfalls.

Characterisation and validation of lactation information from structured electronic health records for use in pharmacoepidemiological studies.

BACKGROUND: Breastfeeding information stored within electronic health records (EHR) has recently been used for pharmacoepidemiological research, however the data are primarily collected for clinical care. OBJECTIVES: To characterise breastfeeding information recorded in structured fields in EHR during infant and postpartum health care visits, and to assess the validity of lactation status based on EHR data versus maternal report at research study visits. METHODS: We assessed breastfeeding information recorded in structured fields in EHR from one health system for a subset of 211 patients who were also enrolled in a study on breast milk composition between 2014 and 2017 that required participants to exclusively breastfeed their infants until at least 1 month of age. We assessed the frequency of breastfeeding information in EHR during the first 12 months of age and compared lactation status based on EHR with maternal report at 1 and 6-month study visits (reference standard). RESULTS: The median number of breastfeeding records in the EHR per infant was six (interquartile range 3) with most observations clustering in the first few weeks of life and around well-infant visits. At the 6-month study visit, 93.8% of participants were breastfeeding and 80.1% were exclusively breastfeeding according to maternal report. Sensitivity of EHR data for identifying ever breastfeeding was at or near 100%, and sensitivity for identifying ever exclusive breastfeeding was 98.0% (95% CI: 95.0%, 99.2%). Sensitivities were 97.3% (95% CI: 93.9%, 98.9%) for identifying any breastfeeding and 94.4% (95% CI: 89.7%, 97.0%) for exclusive breastfeeding, and positive predictive values were 99.5% (95% CI: 97.0%, 99.9%) for any breastfeeding and 95.0% (95% CI: 90.4%, 97.4%) for exclusive breastfeeding. CONCLUSIONS: Breastfeeding information in structured EHR fields have the potential to accurately classify lactation status. The validity of these data should be assessed in populations with a lower breastfeeding prevalence.

Identification of diverticular disease in Swedish healthcare registers: a validation study.

PURPOSE: The Swedish National Patient Register (SNPR) is frequently used in studies of colonic diverticular disease (DD). Despite this, the validity of the coding for this specific disease in the register has not been studied. METHODS: From SNPR, 650 admissions were randomly identified encoded with ICD 10, K572-K579. From the years 2002 and 2010, 323 and 327 patients respectively were included in the validation study. Patients were excluded prior to, or up to 2 years after a diagnosis with IBD, Celiac disease, IBS, all forms of colorectal cancer (primary and secondary), and anal cancer. Medical records were collected and data on clinical findings with assessments, X-ray examinations, endoscopies and laboratory results were reviewed. The basis of coding was compared with internationally accepted definitions for colonic diverticular disease. Positive predictive values (PPV) were calculated. RESULTS: The overall PPV for all diagnoses and both years was 95% (95% CI: 93-96). The PPV for the year 2010 was slightly higher 98% (95% CI: 95-99) than in the year 2002, 91% (95% CI: (87-94) which may be due to the increasing use of computed tomography (CT). CONCLUSION: The validity of DD in SNPR is high, making the SNPR a good source for population-based studies on DD.

Development and validation of the Trust in Multidimensional Healthcare Systems Scale (TIMHSS).

CONTEXT: The COVID-19 pandemic has reignited a commitment from the health policy and health services research communities to rebuilding trust in healthcare and created a renewed appetite for measures of trust for system monitoring and evaluation. The aim of the present paper was to develop a multidimensional measure of trust in healthcare that: (1) Is responsive to the conceptual and methodological limitations of existing measures; (2) Can be used to identify systemic explanations for lower levels of trust in equity-deserving populations; (3) Can be used to design and evaluate interventions aiming to (re)build trust. METHODS: We conducted a 2021 review of existing measures of trust in healthcare, 72 qualitative interviews (Aug-Dec 2021; oversampling for equity-deserving populations), an expert review consensus process (Oct 2021), and factor analyses and validation testing based on two waves of survey data (Nov 2021, n = 694; Jan-Feb 2022, n = 740 respectively). FINDINGS: We present the Trust in Multidimensional Healthcare Systems Scale (TIMHSS); a 38-item correlated three-factor measure of trust in doctors, policies, and the system. Measurement of invariance tests suggest that the TIMHSS can also be reliably administered to diverse populations. CONCLUSIONS: This global measure of trust in healthcare can be used to measure trust over time at a population level, or used within specific subpopulations, to inform interventions to (re)build trust. It can also be used within a clinical setting to provide a stronger evidence base for associations between trust and therapeutic outcomes.

Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation.

BACKGROUND: A real-time model for predicting short-term mortality in critically ill patients is needed to identify patients at imminent risk. However, the performance of the model needs to be validated in various clinical settings and ethnicities before its clinical application. In this study, we aim to develop an ensemble machine learning model using routinely measured clinical variables at a single academic institution in South Korea. METHODS: We developed an ensemble model using deep learning and light gradient boosting machine models. Internal validation was performed using the last two years of the internal cohort dataset, collected from a single academic hospital in South Korea between 2007 and 2021. External validation was performed using the full Medical Information Mart for Intensive Care (MIMIC), eICU-Collaborative Research Database (eICU-CRD), and Amsterdam University Medical Center database (AmsterdamUMCdb) data. The area under the receiver operating characteristic curve (AUROC) was calculated and compared to that for the National Early Warning Score (NEWS). RESULTS: The developed model (iMORS) demonstrated high predictive performance with an internal AUROC of 0.964 (95% confidence interval [CI] 0.963-0.965) and external AUROCs of 0.890 (95% CI 0.889-0.891) for MIMIC, 0.886 (95% CI 0.885-0.887) for eICU-CRD, and 0.870 (95% CI 0.868-0.873) for AmsterdamUMCdb. The model outperformed the NEWS with higher AUROCs in the internal and external validation (0.866 for the internal, 0.746 for MIMIC, 0.798 for eICU-CRD, and 0.819 for AmsterdamUMCdb; p < 0.001). CONCLUSIONS: Our real-time machine learning model to predict short-term mortality in critically ill patients showed excellent performance in both internal and external validations. This model could be a useful decision-support tool in the intensive care units to assist clinicians.

Prospective external validation of IOTA methods for classifying adnexal masses and retrospective assessment of two-step strategy using benign descriptors and ADNEX model: Portuguese multicenter study.

OBJECTIVES: To externally and prospectively validate the International Ovarian Tumor Analysis (IOTA) Simple Rules (SRs), Logistic Regression model 2 (LR2) and Assessment of Different NEoplasias in the adneXa (ADNEX) model in a Portuguese population, comparing these approaches with subjective assessment and the risk-of-malignancy index (RMI), as well as with each other. This study also aimed to retrospectively validate the IOTA two-step strategy, using modified benign simple descriptors (MBDs) followed by the ADNEX model in cases in which MBDs were not applicable. METHODS: This was a prospective multicenter diagnostic accuracy study conducted between January 2016 and December 2021 of consecutive patients with an ultrasound diagnosis of at least one adnexal tumor, who underwent surgery at one of three tertiary referral centers in Lisbon, Portugal. All ultrasound assessments were performed by Level-II or -III sonologists with IOTA certification. Patient clinical data and serum CA 125 levels were collected from hospital databases. Each adnexal mass was classified as benign or malignant using subjective assessment, RMI, IOTA SRs, LR2 and the ADNEX model (with and without CA 125). The reference standard was histopathological diagnosis. In the second phase, all adnexal tumors were classified retrospectively using the two-step strategy (MBDs + ADNEX). Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios and overall accuracy were determined for all methods. Receiver-operating-characteristics curves were constructed and corresponding areas under the curve (AUC) were determined for RMI, LR2, the ADNEX model and the two-step strategy. The ADNEX model calibration plots were constructed using locally estimated scatterplot smoothing (LOESS). RESULTS: Of the 571 patients included in the study, 428 had benign disease and 143 had malignant disease (prevalence of malignancy, 25.0%), of which 42 had borderline ovarian tumor, 93 had primary invasive adnexal cancer and eight had metastatic tumors in the adnexa. Subjective assessment had an overall sensitivity of 97.9% and a specificity of 83.6% for distinguishing between benign and malignant lesions. RMI showed high specificity (95.6%) but very low sensitivity (58.7%), with an AUC of 0.913. The IOTA SRs were applicable in 80.0% of patients, with a sensitivity of 94.8% and specificity of 98.6%. The IOTA LR2 had a sensitivity of 84.6%, specificity of 86.9% and an AUC of 0.939, at a malignancy risk cut-off of 10%. At the same cut-off, the sensitivity, specificity and AUC for the ADNEX model with vs without CA 125 were 95.8% vs 98.6%, 82.5% vs 79.7% and 0.962 vs 0.960, respectively. The ADNEX model gave heterogeneous results for distinguishing between benign masses and different subtypes of malignancy, with the highest AUC (0.991) for discriminating benign masses from primary invasive adnexal cancer Stages II-IV, and the lowest AUC (0.696) for discriminating primary invasive adnexal cancer Stage I from metastatic lesion in the adnexa. The calibration plot suggested underestimation of the risk by the ADNEX model compared with the observed proportion of malignancy. The MBDs were applicable in 26.3% (150/571) of cases, of which none was malignant. The two-step strategy using the ADNEX model in the second step only, with and without CA 125, had AUCs of 0.964 and 0.961, respectively, which was similar to applying the ADNEX model in all patients. CONCLUSIONS: The IOTA methods showed good-to-excellent performance in the Portuguese population, outperforming RMI. The ADNEX model was superior to other methods in terms of accuracy, but interpretation of its ability to distinguish between malignant subtypes was limited by sample size and large differences in the prevalence of tumor subtypes. The IOTA MBDs are reliable in identifying benign disease. The two-step strategy comprising application of MBDs followed by the ADNEX model if MBDs are not applicable, is suitable for daily clinical practice, circumventing the need to calculate the risk of malignancy in all patients. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Psychometric characteristics of a questionnaire to assess perceived disability in people with epilepsy.

It has been determined that quality of life in epilepsy is closely related to the perceived disability experienced by individuals with the diagnosis. However, this measure is seldom considered in healthcare processes. The objective of the present study is to establish the psychometric properties of the Perceived Disability Questionnaire in individuals diagnosed with epilepsy within a Latin American context. A cross-sectional, analytical study was conducted involving 325 participants, aged 12 years and older (M 40.42 years), individuals diagnosed with epilepsy in Colombia. The main psychometric properties of the instrument were explored to account for its factorial validity and reliability. The Perceived Disability Questionnaire exhibits high reliability (α = 0.878) and the three subscales comprising the final version of the questionnaire (Dissatisfaction, Pessimism, and Self-Disdain) explain 45.393 % of the total variance in relation to beliefs of disability associated with the diagnosis of epilepsy; the questionnaire significantly correlates with the Quality of Life in Epilepsy Inventory (QOLIE-10). Adequate psychometric properties of the instrument are found, which allows for its proposal as a tool in epilepsy care processes within the Colombian context.

Cross-cultural validation and psychometric properties of the Spanish version of the quality of life in Childhood epilepsy Questionnaire (QOLCE-55).

OBJECTIVE: The aims of the present study were to translate and validate the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55) into Spanish. METHODS: A backtranslation procedure was used to translate the English version of QOLCE-55 to Spanish, and subsequently, parents of 88 children (aged four to 12 years), completed the Spanish version of three self-reported questionnaires: the QOLCE-55, the Pediatric Quality-of-Life Inventory (PedsQLTM 4.0), and the Pediatric Sleep Questionnaire (PSQ). Seven to 10 days later, parents completed the questionnaires again under the same conditions. RESULTS: Internal consistency was between 0.669 and 0.968 for the four subscales: cognitive (CF), emotional (EF), social (SF) and physical functioning (PF); and 0.954 for the total score. The test-retest reliability assessed with the intraclass correlation coefficient obtained values from 0.683 for SF to 0.962 for CF. The standard error of measurement for the total score was 5.776, and the minimal detectable change was 16.01. Spearman correlations between the total score of the Spanish version of the QOLCE-55 with the subscales was 0.760 for the CF, 0.776 for the EF, 0.799 for the SF, and 0.682 for the PF (p < 0.001). Convergent validity of QOLCE-55 with the PedsQLTM 4.0 scale was -0.962 (p < 0.001), and the discriminant validity of the QOLCE-55 with PSQ was 0.154 (p = 0.272). This version presented a correlation with maximum lifetime consumption of anti-epileptic drugs (0.500; p < 0.001), and current consumption (0.448; p < 0.001). CONCLUSIONS: The Spanish version of the QOLCE-55 has demonstrated adequate psychometric properties, indicating that it can be confidently used to measure the health-related quality-of-life (HRQoL) in children with epilepsy in a Spanish-speaking population. These results corroborate the instrument's cross-cultural validity.

Consistency between the National Patient Register and the Swedish Cancer Register.

PURPOSE: The Swedish National Patient Register (NPR) is widely used as a data source in epidemiological studies, but the consistency of all cancer diagnoses compared to the Swedish Cancer Register (SCR) remains unclear. Using NPR supplementary for detecting safety signals is beneficial due to shorter data extraction delays compared to using SCR alone. This study aims to evaluate the consistency of NPR for cancer diagnoses compared to SCR and its potential use in pharmacoepidemiology. METHODS: Patients with a cancer diagnosis recorded in SCR during 2018-2020 were included. To measure the consistency of NPR diagnoses with SCR as the gold standard, positive predictive value (PPV), and sensitivity were calculated. As an empirical example showing differences in identification of cancer diagnoses in NPR and SCR, two nested case-control studies for the association between antidiabetic medications and pancreatic cancer were repeated using the two registers. Conditional logistic regression was performed and the 95% confidence intervals (CIs) for the odds ratios (ORs) were checked for overlaps. RESULTS: For breast, male genital organs, and oral cancers consistency was high (PPV: 87.5%-97.4%, sensitivity: 82.2%-91.0%), while for female genital organs, thyroid, and ill-defined, secondary, and unspecified sites cancers it was low (PPV: 8.8%-90.0%, sensitivity: 19.9%-32.3%). All the CIs for the ORs from the nested case-control studies overlapped when pancreatic cancer was identified in NPR or SCR. CONCLUSION: Consistency of cancer diagnoses in NPR when compared to SCR depends on cancer type with higher consistency for some cancers and lower for others. Differences in diagnostic processes for different cancer types and coding of cancer in the two registers may explain part of the inconsistent results.

Validation of new medication use algorithms as proxies for worsening disease activity in patients with juvenile idiopathic arthritis.

PURPOSE: To facilitate claims-based research on populations with juvenile idiopathic arthritis (JIA), we sought to validate an algorithm of new medication use as a proxy for worsening JIA disease activity. METHODS: Using electronic health record data from three pediatric centers, we defined new JIA medication use as (re)initiation of disease-modifying antirheumatic drugs or glucocorticoids (oral or intra-articular). Data were collected from 201 randomly selected subjects with (101) or without (100) new medication use. We assessed the positive predictive value (PPV) and negative predictive value (NPV) based on a reference standard of documented worsening of JIA disease activity. The algorithm was refined to optimize test characteristics. RESULTS: Overall, the medication-based algorithm had suboptimal performance in representing worsening JIA disease activity (PPV 69.3%, NPV 77.1%). However, algorithm performance improved for definitions specifying longer times after JIA diagnosis (≥1-year post-diagnosis: PPV 82.9%, NPV 80.0%) or after initiation of prior JIA treatment (≥1-year post-treatment: PPV 89.7%, NPV 80.0%). CONCLUSION: An algorithm for new JIA medication use appears to be a reasonable proxy for worsening JIA disease activity, particularly when specifying new use ≥1 year since initiating a prior JIA medication. This algorithm will be valuable for conducting research on JIA populations within administrative claims databases.