Dataset Information

Clinical parameter-based prediction model for neurosyphilis risk stratification.

ABSTRACT: Accurately predicting neurosyphilis prior to a lumbar puncture (LP) is critical for the prompt management of neurosyphilis. However, a valid and reliable model for this purpose is still lacking. This study aimed to develop a nomogram for the accurate identification of neurosyphilis in patients with syphilis. The training cohort included 9,504 syphilis patients who underwent initial neurosyphilis evaluation between 2009 and 2020, while the validation cohort comprised 526 patients whose data were prospectively collected from January 2021 to September 2021. Neurosyphilis was observed in 35.8% (3,400/9,504) of the training cohort and 37.6% (198/526) of the validation cohort. The nomogram incorporated factors such as age, male gender, neurological and psychiatric symptoms, serum RPR, a mucous plaque of the larynx and nose, a history of other STD infections, and co-diabetes. The model exhibited good performance with concordance indexes of 0.84 (95% CI, 0.83-0.85) and 0.82 (95% CI, 0.78-0.86) in the training and validation cohorts, respectively, along with well-fitted calibration curves. This study developed a precise nomogram to predict neurosyphilis risk in syphilis patients, with potential implications for early detection prior to an LP.

SUBMITTER: Yang Y

PROVIDER: S-EPMC10894895 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical parameter-based prediction model for neurosyphilis risk stratification.

Yang Yilan Y Gu Xin X Zhu Lin L Cheng Yuanyuan Y Lu Haikong H Guan Zhifang Z Shi Mei M Ni Liyan L Peng Ruirui R Zhao Wei W Wu Juan J Qi Tengfei T Long Fuquan F Chai Zhe Z Gong Weiming W Ye Meiping M Zhou Pingyu P

Epidemiology and infection 20240115

Accurately predicting neurosyphilis prior to a lumbar puncture (LP) is critical for the prompt management of neurosyphilis. However, a valid and reliable model for this purpose is still lacking. This study aimed to develop a nomogram for the accurate identification of neurosyphilis in patients with syphilis. The training cohort included 9,504 syphilis patients who underwent initial neurosyphilis evaluation between 2009 and 2020, while the validation cohort comprised 526 patients whose data were ...[more]

PMID: 38224151

Similar Datasets

Project description:ImportanceMultiparametric magnetic resonance imaging (MRI) in conjunction with MRI-transrectal ultrasound (TRUS) fusion-guided biopsies have improved the detection of prostate cancer. It is unclear whether MRI itself adds additional value to multivariable prediction models based on clinical parameters.ObjectiveTo determine whether an MRI-based prediction model can reduce unnecessary biopsies in patients with suspected prostate cancer.Design, setting, and participantsPatients underwent MRI, MRI-TRUS fusion-guided biopsy, and 12-core systematic biopsy in 1 session. The development cohort used to derive the prediction model consisted of 400 patients from 1 institution enrolled between May 14, 2015, and August 31, 2016, and the validation cohort included 251 patients from 2 independent institutions who underwent biopsies between April 1, 2013, and June 30, 2016, at 1 institution and between July 1, 2015, and October 31, 2016, at the other institution. The MRI model included MRI-derived parameters in addition to clinical variables. Area under the curve of receiver operating characteristic curves and decision curve analysis were performed.Main outcomes and measuresRisk of clinically significant prostate cancer on biopsy, defined as a Gleason score of 3 + 4 or higher in at least 1 biopsy core.ResultsOverall, 193 (48.3%) of the 400 patients in the development cohort (mean [SD] age at biopsy, 64.3 [7.1] years) and 96 (38.2%) of the 251 patients in the validation cohort (mean [SD] age at biopsy, 64.9 [7.2] years) had clinically significant prostate cancer, defined as a Gleason score greater than or equal to 3 + 4. By applying the model to the external validation cohort, the area under the curve increased from 64% to 84% compared with the baseline model (P < .001). At a risk threshold of 20%, the MRI model had a lower false-positive rate than the baseline model (46% [95% CI, 32%-66%] vs 92% [95% CI, 70%-100%]), with only a small reduction in the true-positive rate (89% [95% CI, 85%-96%] vs 99% [95% CI, 89%-100%]). Eighteen of 100 fewer biopsies could have been performed, with no increase in the number of patients with missed clinically significant prostate cancers.Conclusions and relevanceThe inclusion of MRI-derived parameters in a risk model could reduce the number of unnecessary biopsies while maintaining a high rate of diagnosis of clinically significant prostate cancers.

Project description:BackgroundUltrasound is widely used in the examination of the parotid gland, but no single ultrasound feature has demonstrated satisfactory diagnostic performance in predicting the nature of parotid nodules. Unlike the established and widely used grading systems for breast and thyroid nodules, a universally adopted and clinically accepted risk stratification system for malignancy in parotid gland nodules remains absent at present. This study aims to establish a malignant risk stratification model for parotid nodules by analyzing patients' clinical features and conventional ultrasound image characteristics.MethodsIn this study, clinical data and ultrasound images of 736 patients with parotid nodules were retrospectively analyzed. Pathological results served as the gold standard, and the patients were randomly divided into training and validation groups in a 7:3 ratio. Clinical and ultrasound features of parotid nodules in the training group were compared. Multifactor logistic regression analysis was employed to screen for risk factors of malignant nodules and quantify scores. The probability of malignant risk was assessed and classified into five grades (Grade 1, normal parotid; Grade 2, definitive benign; Grade 3, possibly benign; Grade 4, suspicious malignant; Grade 5, high probability of malignancy). The diagnostic performance of the model was assessed by using calibration curves, receiver operating characteristic curves, decision curves, and clinical impact curves.ResultsFacial symptoms, unclear margin, irregular shape, microcalcification, and abnormal cervical lymph nodes were independent risk factors for malignant parotid nodules. The area under the curve of the model was 0.850 [95% confidence interval (CI): 0.816-0.879] in the training group and 0.846 (95% CI: 0.791-0.891) in the validation group.ConclusionsThe malignancy risk stratification model based on clinical and ultrasound image features has a good differentiation between benign and malignant parotid nodules, which is helpful for diagnosis and guiding clinical treatment.

Project description:To create a risk model of aging-related long non-coding RNAs (arlncRNAs) and determine whether they might be useful as markers for risk stratification, prognosis prediction, and targeted therapy guidance for patients with lung adenocarcinoma (LUAD). Data on aging genes and lncRNAs from LUAD patients were obtained from Human Aging Genomic Resources 3 and The Cancer Genome Atlas, and differential co-expression analysis of established differentially expressed arlncRNAs (DEarlncRNAs) was performed. They were then paired with a matrix of 0 or 1 by cyclic single pairing. The risk coefficient for each sample of LUAD individuals was obtained, and a risk model was constructed by performing univariate regression, least absolute shrinkage and selection operator regression analysis, and univariate and multivariate Cox regression analysis. Areas under the curve were calculated for the 1-, 3-, and 5-year receiver operating characteristic curves to determine Akaike information criterion-based cutoffs to identify high- and low-risk groups. The survival rate, correlation of clinical characteristics, malignant-infiltrating immune-cell expression, ICI-related gene expression, and chemotherapeutic drug sensitivity were contrasted with the high- and low-risk groups. We found that 99 DEarlncRNAs were upregulated and 12 were downregulated. Twenty pairs of DEarlncRNA pairs were used to create a prognostic model. The 1-, 3-, and 5-year survival curve areas of LUAD individuals were 0.805, 0.793, and 0.855, respectively. The cutoff value to classify patients into two groups was 0.992. The mortality rate was higher in the high-risk group. We affirmed that the LUAD outcome-related independent predictor was the risk score (p < 0.001). Validation of tumor-infiltrating immune cells and ICI-related gene expression differed substantially between the groups. The high-risk group was highly sensitive to docetaxel, erlotinib, gefitinib, and paclitaxel. Risk models constructed from arlncRNAs can be used for risk stratification in patients with LUAD and serve as prognostic markers to identify patients who might benefit from targeted and chemotherapeutic agents.

Project description:BackgroundNumerous tools based on electronic health record (EHR) data that predict risk of unscheduled care and mortality exist. These are often criticised due to lack of external validation, potential for low predictive ability and the use of thresholds that can lead to large numbers being escalated for assessment that would not have an adverse outcome leading to unsuccessful active case management. Evidence supports the importance of clinical judgement in risk prediction particularly when ruling out disease. The aim of this pilot study was to explore performance analysis of a digitally driven risk stratification model combined with GP clinical judgement to identify patients with escalating urgent care and mortality events.MethodsClinically risk stratified cohort study of 6 GP practices in a deprived, multi-ethnic UK city. Initial digital driven risk stratification into Escalated and Non-escalated groups used 7 risk factors. The Escalated group underwent stratification using GP global clinical judgement (GCJ) into Concern and No concern groupings.Results3968 out of 31,392 patients were data stratified into the Escalated group and further categorised into No concern (n = 3450 (10.9%)) or Concern (n = 518 (1.7%)) by GPs. The 30-day combined event rate (unscheduled care or death) per 1,000 was 19.0 in the whole population, 67.8 in the Escalated group and 168.0 in the Concern group (p < 0.001). The de-escalation effect of GP assessment into No Concern versus Concern was strongly negatively predictive (OR 0.25 (95%CI 0.19-0.33; p < 0.001)). The whole population ROC for the global approach (Non-escalated, GP No Concern, GP Concern) was 0.614 (0.592-0.637), p < 0.001, and the increase in the ROC area under the curve for 30-day events was all focused here (+ 0.4% (0.3-0.6%, p < 0.001), translating into a specific ROC c-statistic for GP GCJ of 0.603 ((0.565-0.642), p < 0.001).ConclusionsThe digital only component of the model performed well but adding GP clinical judgement significantly improved risk prediction, particularly by adding negative predictive value.

Dataset Information

Clinical parameter-based prediction model for neurosyphilis risk stratification.

Publications

Clinical parameter-based prediction model for neurosyphilis risk stratification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets