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INTRODUCTION: Many patients offered case management services to address their health and social needs choose not to engage. Factors that drive engagement remain unclear. We sought to understand patient characteristics associated with engagement in a social needs case management program and variability by case manager. METHODS: Between August 2017 and February 2021, 43,347 Medicaid beneficiaries with an elevated risk of hospital or emergency department use were offered case management in Contra Costa County, California. Results were analyzed in 2022 using descriptive statistics and multilevel logistic regression models to examine 1) associations between patient engagement and patient characteristics and 2) variation in engagement attributable to case managers. Engagement was defined as responding to case manager outreach and documentation of at least 1 topic to mutually address. A sensitivity analysis was performed by stratifying the pre-COVID-19 and COVID-19 cohorts. RESULTS: A total of 16,811 (39%) of eligible patients engaged. Adjusted analyses indicate associations between higher patient engagement and female gender, age 40 and over, Black/African American race, Hispanic/Latino ethnicity, history of homelessness, and a medical history of certain chronic conditions and depressive disorder. The intraclass correlation coefficient indicates that 6% of the variation in engagement was explained at the case manager level. CONCLUSIONS: Medicaid patients with a history of housing instability and specific medical conditions were more likely to enroll in case management services, consistent with prior evidence that patients with greater need are more receptive to assistance. Case managers accounted for a small percentage of variation in patient engagement.
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COVID-19 , Manejo de Caso , Medicaid , Participación del Paciente , Humanos , Femenino , Masculino , Manejo de Caso/organización & administración , Manejo de Caso/estadística & datos numéricos , Participación del Paciente/estadística & datos numéricos , Adulto , COVID-19/epidemiología , Persona de Mediana Edad , California , Estados Unidos , Medicaid/estadística & datos numéricos , SARS-CoV-2 , Adulto Joven , Personas con Mala Vivienda/estadística & datos numéricos , Anciano , AdolescenteRESUMEN
INTRODUCTION: A dynamic molecular biomarker that can identify early efficacy of immune checkpoint inhibitor (ICI) therapy remains an unmet clinical need. Here we evaluate if a novel circulating tumor DNA (ctDNA) assay, xM, used for treatment response monitoring (TRM), that quantifies changes in ctDNA tumor fraction (TF), can predict outcome benefits in patients treated with ICI alone or in combination with chemotherapy in a real-world (RW) cohort. METHODS: This retrospective study consisted of patients with advanced cancer from the Tempus de-identified clinical genomic database who received longitudinal liquid-based next-generation sequencing. Eligible patients had a blood sample ≤ 40 days prior to the start of ICI initiation and an on-treatment blood sample 15-180 days post ICI initiation. TF was calculated via an ensemble algorithm that utilizes TF estimates derived from variants and copy number information. Patients with molecular response (MR) were defined as patients with a ≥ 50% decrease in TF between tests. In the subset of patients with rw-imaging data between 2 and 18 weeks of ICI initiation, the predictive value of MR in addition to rw-imaging was compared to a model of rw-imaging alone. RESULTS: The evaluable cohort (N = 86) was composed of 14 solid cancer types. Patients received either ICI monotherapy (38.4%, N = 33) or ICI in combination with chemotherapy (61.6%, N = 53). Patients with MR had significantly longer rw-overall survival (rwOS) (hazard ratio (HR) 0.4, P = 0.004) and rw-progression free survival (rwPFS) (HR 0.4, P = 0.005) than patients with molecular non-response (nMR). Similar results were seen in the ICI monotherapy subcohort; HR 0.2, P = 0.02 for rwOS and HR 0.2, P = 0.01 for rwPFS. In the subset of patients with matched rw-imaging data (N = 51), a model incorporating both MR and rw-imaging was superior in predicting rwOS than rw-imaging alone (P = 0.02). CONCLUSIONS: xM used for TRM is a novel serial quantitative TF algorithm that can be used clinically to evaluate ICI therapy efficacy.
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Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson's Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.
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OBJECTIVES: Explore whether socioeconomic differences of patients affect the prioritisation of pre-existing research questions and explore the agreement between healthcare professionals (HCP) and patients in priority setting partnerships (PSPs). DESIGN AND SETTING: Prospective, three centre survey across UK (400 participants), Tuebingen (176 participants) and Luxembourg (303 participants). People with Parkinson's (PwP), research participants, relatives and HCP associated with three Parkinson's cohort studies were invited to participate, along with linked centres (clinical care settings, research groups, charities). Responders were encouraged to pass on the survey to friends/families/carers. METHODS: The survey involved rating the importance of research questions on a Likert scale, allowing for the generation of one new question participants felt was particularly important. Collection of demographic information allowed for comparisons of priorities across a range of socioeconomic variables; the top 10 research priorities for each group were then compared. Questions added by participants were subject to a thematic analysis. RESULTS: 879 participants completed the survey (58% PwP, 22% family/friends, 13% HCP, 4% carers). Finding the best form of physiotherapy for PwP was the number one priority across the majority of analyses. HCP were the only subgroup not to place physiotherapy in the top 10. Factors most likely to affect prioritisation in PwP included educational level, presence of carer support and disease duration. There was little difference between other socioeconomic categories. CONCLUSIONS: Socioeconomic factors modestly influenced some research priority ratings but did not significantly affect the top priority in most comparisons. Future studies must ensure patients from a range of socioeconomic backgrounds are recruited, ensuring results generalisable to the public while also identifying any key disparities in prioritisation. PSP should also take care that HCP do not skew results during prioritisation of questions, as in this study the most important priority to patients was not identified by professionals.
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Investigación Biomédica , Enfermedad de Parkinson , Prioridades en Salud , Humanos , Enfermedad de Parkinson/terapia , Estudios Prospectivos , Factores Socioeconómicos , Encuestas y CuestionariosRESUMEN
Tissue-based next-generation sequencing (NGS) in metastatic breast cancer (mBC) is limited by the inability to noninvasively track tumor evolution. Cell-free DNA (cfDNA) NGS has made sequential testing feasible; however, the relationship between cfDNA and tissue-based testing in mBC is not well understood. Here, we evaluate concordance between tissue and cfDNA NGS relative to cfDNA sampling frequency in a large, clinically annotated mBC data set. METHODS: Tempus LENS was used to analyze deidentified records of mBC cases with Tempus xT (tissue) and xF (cfDNA) sequencing results. Then, various metrics of concordance were assessed within overlapping probe regions of the tissue and cfDNA assays (104 genes), focusing on pathogenic variants. Variant allele frequencies of discordant and concordant pathogenic variants were also compared. Analyses were stratified by mBC subtype and time between tests. RESULTS: Records from 300 paired tissue and liquid biopsies were analyzed. Median time between tissue and blood collection was 78.5 days (standard deviation = 642.99). The median number of pathogenic variants/patient was one for cfDNA and two for tissue. Across the cohort, 77.8% of pathogenic tissue variants were found in cfDNA and 75.7% of pathogenic cfDNA variants were found in tissue when tests were ≤ 7 days apart, which decreased to 50.3% and 51.8%, respectively, for > 365 days. Furthermore, the median patient-level variant concordance was 67% when tests were ≤7 days apart and 30%-37% when > 30 days. The median variant allele frequencies of discordant variants were generally lower than those of concordant variants within the same time frame. CONCLUSION: We observed high concordances between tissue and cfDNA results that generally decreased with longer durations between tests. Thus, cfDNA NGS reliably measures tissue genomics and is likely beneficial for longitudinal monitoring of molecular changes in mBC.
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Neoplasias de la Mama , Ácidos Nucleicos Libres de Células , Neoplasias de la Mama/genética , Ácidos Nucleicos Libres de Células/genética , Femenino , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Biopsia Líquida , MutaciónRESUMEN
BACKGROUND: Parkinson's disease (PD) is a multi-system disorder consisting of not only classic motor symptoms but also a variety of non-motor symptoms including gastrointestinal (GI) dysfunction and mood disorders. The gut microbiota has been suggested to play a role in modulating PD motor and non-motor features, although the causality and mechanisms behind these proposed interactions remains largely understudied. OBJECTIVE: In this study, we aimed to provide in-depth characterization of an established mouse model of PD (transgenic (TG) SNCA A53T) and experimentally address how changes to the gut microbiota impact the PD-like phenotype. METHODS: We profiled the PD-like phenotype of transgenic mice through a panel of motor, GI, and behavioral tests. We then investigated how antibiotic treatment or gut microbial community transfer (via cohousing with wild-type mice) impacted the PD-like phenotype. RESULTS: We found that this mouse model demonstrated early (6 weeks of age) motor symptoms when compared to a wild-type control mouse strain. Transgenic mice also exhibited early GI dysfunction, as well as behavioral alterations, including reduced anxiety-like behavior, and increased depression-like and apathy-like behavior. Compared to wild-type mice, the transgenic fecal microbiota was less diverse and compositionally distinct. Interestingly, drastic alterations to the gut microbiota, through antibiotic treatment or cohousing with wild-type mice, had a minimal effect on the motor, GI, and behavioral phenotype of transgenic mice. CONCLUSION: We concluded that this mouse model effectively recapitulates motor and non-motor features of PD; however, the gut microbiota appears to exhibit a minor impact on the pathophysiology of this PD model.
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Enfermedades Gastrointestinales , Microbioma Gastrointestinal , Enfermedad de Parkinson , Animales , Antibacterianos , Modelos Animales de Enfermedad , Microbioma Gastrointestinal/fisiología , Ratones , Ratones Transgénicos , FenotipoRESUMEN
BACKGROUND: An unmet need remains for sensitive outcome measures in neuroprotective trials. The study aims to determine whether a composite clinical motor score, combining the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III motor examination score, Purdue Pegboard Test, and Timed Up and Go, provides greater sensitivity in detecting motor change in early disease than the MDS-UPDRS III alone. METHODS: The Oxford Discovery longitudinal cohort study involves individuals with isolated rapid eye movement sleep behaviour disorder (iRBD) (n=272, confirmed polysomnographically, median follow-up: 1.6 years), idiopathic Parkinson's disease (PD) (n=909, median follow-up: 3.5 years, baseline: <3.5 years disease duration) and controls (n=316, age-matched and sex-matched, without a first-degree family history of PD). Motor and non-motor assessments were performed at each in-person visit. RESULTS: Compared with the MDS-UPDRS III, the composite clinical motor score demonstrated a wider score distribution in iRBD and controls, lower coefficient of variation (37% vs 67%), and higher correlation coefficients with self-reported measures of motor severity (0.65 vs 0.61) and overall health status (-0.40 vs -0.33). Greater score range in mild to moderate PD, higher magnitude of longitudinal change in iRBD and longitudinal score linearity suggest better sensitivity in detecting subtle motor change. The composite clinical motor score was more accurate than the MDS-UPDRS III in predicting clinical outcomes, requiring 64% fewer participants with PD and 51% fewer participants with iRBD in sample size estimations for a hypothetical 18-month placebo-controlled clinical trial. CONCLUSION: The composite clinical motor score may offer greater consistency and sensitivity in detecting change than the MDS-UPDRS III.
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Enfermedad de Parkinson , Trastorno de la Conducta del Sueño REM , Humanos , Estudios Longitudinales , Pruebas de Estado Mental y Demencia , Evaluación de Resultado en la Atención de Salud , Enfermedad de Parkinson/diagnóstico , Índice de Severidad de la EnfermedadRESUMEN
We report the first published case of Prototheca wickerhamii breast implant infection. This occurred after mastectomy, chemotherapy, radiotherapy, breast reconstruction, implant revisions and breast seroma aspirations and was preceded by polymicrobial infection. Definitive treatment required implant removal and intravenous liposomal amphotericin B. The management of breast prosthesis infections is discussed.
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BACKGROUND: Parkinson's disease (PD) comorbid with rapid eye movement sleep behavior disorder (RBD) may show more severe motor and nonmotor symptoms, suggesting a distinct PD subtype. OBJECTIVE: The aim of this study was to investigate the impact of RBD on the longitudinal change of motor and nonmotor symptoms in patients with PD. METHODS: Patients with early PD (diagnosed within 3.5 years) recruited from 2010 to 2019 were followed every 18 months in the Oxford Parkinson's Disease Centre Discovery cohort. At each visit, we used standard questionnaires and measurements to assess demographic features and motor and nonmotor symptoms (including RBD, daytime sleepiness, mood, autonomic symptoms, cognition, and olfaction). Data were analyzed with linear mixed effects and Cox regression models. Possible RBD (pRBD) was longitudinally determined according to RBD Screening Questionnaire scores. RESULTS: A total of 923 patients were recruited (mean age: 67.1 ± 9.59 years; 35.9% female), and 788 had follow-up assessment(s) (mean: 4.8 ± 1.98 years, range: 1.3-8.3). Among them, 33.3% were identified as pRBD (PD + pRBD). Patients with PD + pRBD had more severe baseline symptoms and showed faster progression on Movement Disorder Society-Unified Parkinson's Disease Rating Scale parts I and III, Purdue Pegboard test, and Beck Depression Inventory scores. Moreover, PD + pRBD was associated with an increased level of risk for mild cognitive impairment (hazard ratio [HR] = 1.36, 95% confidence interval [CI]: 1.01-1.83), freezing of gait (HR = 1.42, 95% CI: 1.10-1.86), and frequent falling (HR = 1.62, 95% CI: 1.02-2.60). CONCLUSIONS: Patients with PD + pRBD progress faster on motor, mood, and cognitive symptoms, confirming a more aggressive PD subtype that can be identified at baseline and has major clinical implications. © 2021 International Parkinson and Movement Disorder Society.
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Trastornos Neurológicos de la Marcha , Enfermedad de Parkinson , Trastorno de la Conducta del Sueño REM , Anciano , Estudios de Cohortes , Femenino , Trastornos Neurológicos de la Marcha/complicaciones , Humanos , Masculino , Persona de Mediana Edad , Enfermedad de Parkinson/complicaciones , Enfermedad de Parkinson/psicología , Trastorno de la Conducta del Sueño REM/complicaciones , Trastorno de la Conducta del Sueño REM/diagnóstico , Encuestas y CuestionariosRESUMEN
Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.
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Prueba de COVID-19 , COVID-19 , Aprendizaje Automático , Modelos Biológicos , SARS-CoV-2/metabolismo , Adolescente , Adulto , Biomarcadores/sangre , COVID-19/sangre , COVID-19/diagnóstico por imagen , Femenino , Humanos , Masculino , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Estudios Retrospectivos , Tórax/diagnóstico por imagenRESUMEN
Patients with isolated rapid-eye-movement sleep behaviour disorder (RBD) are commonly regarded as being in the early stages of a progressive neurodegenerative disease involving α-synuclein pathology, such as Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. Abnormal α-synuclein deposition occurs early in the neurodegenerative process across the central and peripheral nervous systems and might precede the appearance of motor symptoms and cognitive decline by several decades. These findings provide the rationale to develop reliable biomarkers that can better predict conversion to clinically manifest α-synucleinopathies. In addition, biomarkers of disease progression will be essential to monitor treatment response once disease-modifying therapies become available, and biomarkers of disease subtype will be essential to enable prediction of which subtype of α-synucleinopathy patients with isolated RBD might develop.
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Biomarcadores , Trastorno de la Conducta del Sueño REM/diagnóstico , Sinucleinopatías/diagnóstico , Progresión de la Enfermedad , Humanos , Pronóstico , Trastorno de la Conducta del Sueño REM/complicaciones , Sinucleinopatías/etiología , alfa-SinucleínaRESUMEN
Liquid biopsy is a valuable precision oncology tool that is increasingly used as a non-invasive approach to identify biomarkers, detect resistance mutations, monitor disease burden, and identify early recurrence. The Tempus xF liquid biopsy assay is a 105-gene, hybrid-capture, next-generation sequencing (NGS) assay that detects single-nucleotide variants, insertions/deletions, copy number variants, and chromosomal rearrangements. Here, we present extensive validation studies of the xF assay using reference standards, cell lines, and patient samples that establish high sensitivity, specificity, and accuracy in variant detection. The Tempus xF assay is highly concordant with orthogonal methods, including ddPCR, tumor tissue-based NGS assays, and another commercial plasma-based NGS assay. Using matched samples, we developed a dynamic filtering method to account for germline mutations and clonal hematopoiesis, while significantly decreasing the number of false-positive variants reported. Additionally, we calculated accurate circulating tumor fraction estimates (ctFEs) using the Off-Target Tumor Estimation Routine (OTTER) algorithm for targeted-panel sequencing. In a cohort of 1,000 randomly selected cancer patients who underwent xF testing, we found that ctFEs correlated with disease burden and clinical outcomes. These results highlight the potential of serial testing to monitor treatment efficacy and disease course, providing strong support for incorporating liquid biopsy in the management of patients with advanced disease.
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History A 46-year-old woman with known mixed connective tissue disease with clinical features of scleroderma and polymyositis and who was not on specific medications was referred to our institution to assess for interstitial lung disease due to her predisposing condition. She was a nonsmoker, had no respiratory symptoms, and enjoyed good exercise tolerance. She did not have any cutaneous lesions or renal disease. There was no family history of pulmonary or systemic disease. Her routine blood test results revealed a white blood cell count of 4.6 × 109/L (normal range, [4.4-10.1] × 109/L), a hemoglobin level of 7.76 mmol/L (normal range, 7.26-9.18 mmol/L), a platelet count of 189 × 109/L (normal range, [170-380] × 109/L), a bilirubin level of 8 mmol/L (normal range, <19 mmol/L), and a creatinine level of 63 mmol/L (normal range, 45-82 mmol/L), all within normal limits. Lung function tests at presentation yielded normal results, with a diffusing capacity for carbon monoxide of 95% and a forced vital capacity of 2.29 (98% predicted value). However, this patient had an elevated serum globulin level of 47 g/L (normal range, 26-32 g/L) and an erythrocyte sedimentation rate of 36 mm/h (normal range, 0-20 mm/h), while C-reactive protein level was normal at less than 0.35 mg/dL. She was seropositive for antinuclear (titer >1/720), anti-Ro, anti-La, and anti-extractable nuclear antigen antibodies. Chest radiography and CT were performed at presentation and 14-year follow-up. PET/CT was performed at 7- and 13-year follow-up. Throughout this 14-year follow-up period, she remained completely free of respiratory symptoms and continued to go for a brisk walk every day. At 14-year follow-up, there was no substantial change in serum laboratory values, but a lung function test revealed her diffusing capacity for carbon monoxide had decreased to 52%, while her forced vital capacity remained good at 95%; these findings were suggestive of interval development of restrictive lung function.
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Amiloidosis/diagnóstico por imagen , Quistes/diagnóstico por imagen , Enfermedades Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/diagnóstico por imagen , Linfoma de Células B de la Zona Marginal/diagnóstico por imagen , Biomarcadores/sangre , Diagnóstico Diferencial , Progresión de la Enfermedad , Femenino , Humanos , Persona de Mediana Edad , Tomografía Computarizada por Tomografía de Emisión de Positrones , Radiografía Torácica , Pruebas de Función Respiratoria , Tomografía Computarizada por Rayos XRESUMEN
INTRODUCTION: Parkinson's disease (PD) is a common neurodegenerative disorder with substantial morbidity. No disease-modifying treatments currently exist. The glucagon like peptide-1 receptor agonist exenatide has been associated in single-centre studies with reduced motor deterioration over 1 year. The aim of this multicentre UK trial is to confirm whether these previous positive results are maintained in a larger number of participants over 2 years and if effects accumulate with prolonged drug exposure. METHODS AND ANALYSIS: This is a phase 3, multicentre, double-blind, randomised, placebo-controlled trial of exenatide at a dose of 2 mg weekly in 200 participants with mild to moderate PD. Treatment duration is 96 weeks. Randomisation is 1:1, drug to placebo. Assessments are performed at baseline, week 12, 24, 36, 48, 60, 72, 84 and 96 weeks.The primary outcome is the comparison of Movement Disorders Society Unified Parkinson's Disease Rating Scale part 3 motor subscore in the practically defined OFF medication state at 96 weeks between participants according to treatment allocation. Secondary outcomes will compare the change between groups among other motor, non-motor and cognitive scores. The primary outcome will be reported using descriptive statistics and comparisons between treatment groups using a mixed model, adjusting for baseline scores. Secondary outcomes will be summarised between treatment groups using summary statistics and appropriate statistical tests to assess for significant differences. ETHICS AND DISSEMINATION: This trial has been approved by the South Central-Berkshire Research Ethics Committee and the Health Research Authority. Results will be disseminated in peer-reviewed journals, presented at scientific meetings and to patients in lay-summary format. TRIAL REGISTRATION NUMBERS: NCT04232969, ISRCTN14552789.
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Enfermedad de Parkinson , Ensayos Clínicos Fase III como Asunto , Método Doble Ciego , Exenatida , Humanos , Estudios Multicéntricos como Asunto , Enfermedad de Parkinson/tratamiento farmacológico , Ensayos Clínicos Controlados Aleatorios como Asunto , Resultado del TratamientoRESUMEN
History A 46-year-old woman with known mixed connective tissue disease with clinical features of scleroderma and polymyositis and who was not on specific medications was referred to our institution to assess for interstitial lung disease due to her predisposing condition. She was a nonsmoker, had no respiratory symptoms, and enjoyed good exercise tolerance. She did not have any cutaneous lesions or renal disease. There was no family history of pulmonary or systemic disease. Her routine blood test results revealed a white blood cell count of 4.6 × 109/L (normal range, [4.4-10.1] × 109/L), a hemoglobin level of 7.76 mmol/L (normal range, 7.26-9.18 mmol/L), a platelet count of 189 × 109/L (normal range, [170-380] × 109/L), a bilirubin level of 8 µmol/L (7-19 µmol/L), and a creatinine level of 63 µmol/L (45-82 µmol/L), all within normal limits. Lung function tests at presentation yielded normal results, with a diffusing capacity for carbon monoxide of 95% and a forced vital capacity of 2.29 (98% predicted value). However, this patient had an elevated serum globulin level of 47 g/L (normal range, 26-32 g/L) and an erythrocyte sedimentation rate of 36 mm/h (normal range, 0-20 mm/h), while C-reactive protein level was normal at less than 0.35 mg/dL. She was seropositive for antinuclear (titer >1/720), anti-Ro, anti-La, and anti-extractable nuclear antigen antibodies. Chest radiography and CT were performed at presentation (Figs 1, 2) and 14-year follow-up (Figs 3, 4). PET/CT was performed at 7- (Fig 5, A and B) and 13-year follow-up (Fig 5, C and D). Throughout this 14-year follow-up period, she remained completely free of respiratory symptoms and continued to go for a brisk walk every day. At 14-year follow-up, there was no substantial change in serum laboratory values, but a lung function test revealed her diffusing capacity for carbon monoxide had decreased to 52%, while her forced vital capacity remained good at 95%; these findings were suggestive of interval development of restrictive lung function.
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OBJECTIVE: Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This study investigated the use of a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors. METHODS: Polysomnography signals from 50 participants with RBD and 50 age-matched healthy controls were used to evaluate this study. Three stage sleep classification was achieved using a random forest classifier and features derived from a combination of cost-effective and easy to use sensors, namely electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG) channels. Subsequently, RBD detection was achieved using established and new metrics derived from ECG and EMG channels. RESULTS: The EOG and EMG combination provided the optimal minimalist fully-automated performance, achieving 0.57 ± 0.19 kappa (3 stage) for sleep staging and an RBD detection accuracy of 0.90 ± 0.11, (sensitivity and specificity of 0.88 ± 0.13 and 0.92 ± 0.098, respectively). A single ECG sensor achieved three state sleep staging with 0.28 ± 0.06 kappa and RBD detection accuracy of 0.62 ± 0.10. CONCLUSIONS: This study demonstrates the feasibility of using signals from a single EOG and EMG sensor to detect RBD using fully-automated techniques. SIGNIFICANCE: This study proposes a cost-effective, practical, and simple RBD identification support tool using only two sensors (EMG and EOG); ideal for screening purposes.
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Electroencefalografía/métodos , Electromiografía/métodos , Electrooculografía/métodos , Polisomnografía/métodos , Trastorno de la Conducta del Sueño REM/diagnóstico , Sueño REM/fisiología , Anciano , Femenino , Humanos , Masculino , Tamizaje Masivo , Persona de Mediana Edad , Trastorno de la Conducta del Sueño REM/fisiopatología , Sensibilidad y EspecificidadRESUMEN
OBJECTIVE: We sought to identify an abbreviated test of impaired olfaction amenable for use in busy clinical environments in prodromal (isolated REM sleep behavior disorder [iRBD]) and manifest Parkinson disease (PD). METHODS: Eight hundred ninety individuals with PD and 313 controls in the Discovery cohort study underwent Sniffin' Stick odor identification assessment. Random forests were initially trained to distinguish individuals with poor (functional anosmia/hyposmia) and good (normosmia/super-smeller) smell ability using all 16 Sniffin' Sticks. Models were retrained using the top 3 sticks ranked by order of predictor importance. One randomly selected 3-stick model was tested in a second independent PD dataset (n = 452) and in 2 iRBD datasets (Discovery n = 241, Marburg n = 37) before being compared to previously described abbreviated Sniffin' Stick combinations. RESULTS: In differentiating poor from good smell ability, the overall area under the curve (AUC) value associated with the top 3 sticks (anise/licorice/banana) was 0.95 in the Development dataset (sensitivity 90%, specificity 92%, positive predictive value 92%, negative predictive value 90%). Internal and external validation confirmed AUCs ≥0.90. The combination of the 3-stick model determined poor smell, and an RBD screening questionnaire score of ≥5 separated those with iRBD from controls with a sensitivity, specificity, positive predictive value, and negative predictive value of 65%, 100%, 100%, and 30%. CONCLUSIONS: Our 3-Sniffin'-Stick model holds potential utility as a brief screening test in the stratification of individuals with PD and iRBD according to olfactory dysfunction. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that a 3-Sniffin'-Stick model distinguishes individuals with poor and good smell ability and can be used to screen for individuals with iRBD.
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Aprendizaje Automático , Examen Neurológico/métodos , Trastornos del Olfato/diagnóstico , Trastornos del Olfato/etiología , Enfermedad de Parkinson/diagnóstico , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Enfermedad de Parkinson/complicaciones , Trastorno de la Conducta del Sueño REM/diagnóstico , Sensibilidad y Especificidad , Umbral SensorialRESUMEN
OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.
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COVID-19/diagnóstico , SARS-CoV-2 , Adulto , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , COVID-19/etiología , Femenino , Hospitales , Humanos , Modelos Logísticos , Masculino , Persona de Mediana Edad , Nomogramas , ProbabilidadRESUMEN
PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR). MATERIALS AND METHODS: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC). RESULTS: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test). CONCLUSIONS: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.
Asunto(s)
COVID-19/diagnóstico por imagen , Aprendizaje Profundo , Pulmón/diagnóstico por imagen , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Radiografía Torácica/métodos , Adulto , Anciano , Algoritmos , Femenino , Humanos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , SARS-CoV-2 , Sensibilidad y Especificidad , Adulto JovenRESUMEN
PURPOSE: The coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic. CT although sensitive in detecting changes suffers from poor specificity in discrimination from other causes of ground glass opacities (GGOs). We aimed to develop and validate a CT-based radiomics model to differentiate COVID-19 from other causes of pulmonary GGOs. METHODS: We retrospectively included COVID-19 patients between 24/01/2020 and 31/03/2020 as case group and patients with pulmonary GGOs between 04/02/2012 and 31/03/2020 as a control group. Radiomics features were extracted from contoured GGOs by PyRadiomics. The least absolute shrinkage and selection operator method was used to establish the radiomics model. We assessed the performance using the area under the curve of the receiver operating characteristic curve (AUC). RESULTS: A total of 301 patients (age mean⯱â¯SD: 64⯱â¯15 years; male: 52.8 %) from three hospitals were enrolled, including 33 COVID-19 patients in the case group and 268 patients with malignancies or pneumonia in the control group. Thirteen radiomics features out of 474 were selected to build the model. This model achieved an AUC of 0.905, accuracy of 89.5 %, sensitivity of 83.3 %, specificity of 90.0 % in the testing set. CONCLUSION: We developed a noninvasive radiomics model based on CT imaging for the diagnosis of COVID-19 based on GGO lesions, which could be a promising supplementary tool for improving specificity for COVID-19 in a population confounded by ground glass opacity changes from other etiologies.