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BackgroundStroke is the second leading cause of death worldwide and a major cause of long-term neurological disability, imposing an enormous financial burden on families and society. This study aimed to identify the predictors in stroke patients and construct a nomogram prediction model based on these predictors.MethodsThis retrospective study included 11,435 participants aged >20 years who were selected from the NHANES 2011–2018. Randomly selected subjects (n = 8531; 75%) and the remaining subjects comprised the development and validation groups, respectively. The least absolute shrinkage and selection operator (LASSO) binomial and logistic regression models were used to select the optimal predictive variables. The stroke probability was calculated using a predictor-based nomogram. Nomogram performance was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve with 1000 bootstrap resample validations. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram.ResultsAccording to the minimum criteria of non-zero coefficients of Lasso and logistic regression screening, older age, lower education level, lower family income, hypertension, depression status, diabetes, heavy smoking, heavy drinking, trouble sleeping, congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris and myocardial infarction were independently associated with a higher stroke risk. A nomogram model for stroke patient risk was established based on these predictors. The AUC (C statistic) of the nomogram was 0.843 (95% CI: 0.8186–0.8430) in the development group and 0.826 (95% CI: 0.7811, 0.8716) in the validation group. The calibration curves after 1000 bootstraps displayed a good fit between the actual and predicted probabilities in both the development and validation groups. DCA showed that the model in the development and validation groups had a net benefit when the risk thresholds were 0–0.2 and 0–0.25, respectively.DiscussionThis study effectively established a nomogram including demographic characteristics, vascular risk factors, emotional factors and lifestyle behaviors to predict stroke risk. This nomogram is helpful for screening high-risk stroke individuals and could assist physicians in making better treatment decisions to reduce stroke occurrence.

BackgroundAlthough non-motor symptoms of Parkinson’s disease (PD) are serious, effective treatments are still lacking. Acupuncture may have clinical benefits for non-motor symptoms of PD patients, but high-quality evidence supporting this possibility is still limited. Hence, we conducted this meta-analysis to evaluate the effect of acupuncture treatment on non-motor symptoms in patients with PD.MethodsRandomized controlled trials (RCTs) of acupuncture treatment for PD were retrieved from the following electronic databases: Medline (OVID), Embase (OVID), Cochrane Library, Web of Science, China National Knowledge Infrastructure, Chinese BioMedical Literature Database, Chonqing VIP (CQVIP), and Wangfang database. Studies evaluating non-motor symptoms of PD were retrieved. Methodological quality was assessed using the Cochrane Handbook for Systematic Reviews of Interventions.ResultsA total of 27 RCTs were included, among which 8 outcomes related to non-motor symptoms were evaluated. The results showed that acupuncture combined with medication had benefits for PD-related insomnia relative to medication alone or sham acupuncture [standardized mean difference (SMD) = 0.517; 95% confidence interval (CI) = 0.242–0.793; p = 0.000], and acupuncture treatment had benefits at 8 weeks (SMD = 0.519; 95% CI = 0.181–0.857; p = 0.003). Regarding depression, acupuncture treatment was more effective (SMD = −0.353; 95% CI = −0.669 to −0.037; p = 0.029) within 2 months (SMD = −0.671; 95% CI = −1.332 to −0.011; p = 0.046). Regarding cognition, quality of life, and Unified Parkinson’s Disease Rating Scale (UPDRS) I and II scores, acupuncture treatment was effective [SMD = 0.878, 95% CI = 0.046–1.711, p = 0.039; SMD = −0.690, 95% CI = −1.226 to −0.155, p = 0.011; weighted mean difference (WMD) = −1.536, 95% CI = −2.201 to −0.871, p = 0.000; WMD = −2.071, 95% CI = −3.792 to −0.351, p = 0.018; respectively]. A significant difference was not found in terms of PD-related constipation. Only one study evaluated PD-related fatigue.ConclusionThe results of the analysis suggested that acupuncture treatment could ameliorate the symptoms of depression, quality of life, cognition, total mentation, behavior and mood, and activities of daily living in PD patients. Nevertheless, more prospective, well-designed RCTs with larger sample sizes are required to confirm our findings.