Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography: a multivariable predictive model study

Background:Computed tomography images are easy to misjudge because of their complexity, especially images of solitary pulmonary nodules, of which diagnosis as benign or malignant is extremely important in lung cancer treatment. Therefore, there is an urgent need for a more effective strategy in lung cancer diagnosis. In our study, we aimed to externally validate and revise the Mayo model, and a new model was established.Methods:A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training, internal validation, and external validation sets (n = 849, 365, and 236, respectively). External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training set. Overall performance of each model was evaluated using area under receiver operating characteristic curve (AUC). Finally, the model validation was completed on the validation data set.Results:The AUC of the Mayo model on the training set was 0.653 (95% confidence interval [CI]: 0.613–0.694). After re-estimation of the coefficients of all covariates included in the original Mayo model, the revised Mayo model achieved an AUC of 0.671 (95% CI: 0.635–0.706). We then developed a new model that achieved a higher AUC of 0.891 (95% CI: 0.865–0.917). It had an AUC of 0.888 (95% CI: 0.842–0.934) on the internal validation set, which was significantly higher than that of the revised Mayo model (AUC: 0.577, 95% CI: 0.509–0.646) and the Mayo model (AUC: 0.609, 95% CI, 0.544–0.675) (P < 0.001). The AUC of the new model was 0.876 (95% CI: 0.831–0.920) on the external verification set, which was higher than the corresponding value of the Mayo model (AUC: 0.705, 95% CI: 0.639–0.772) and revised Mayo model (AUC: 0.706, 95% CI: 0.640–0.772) (P < 0.001). Then the prediction model was presented as a nomogram, which is easier to generalize.Conclusions:After external verification and recalibration of the Mayo model, the results show that they are not suitable for the prediction of malignant pulmonary nodules in the Chinese population. Therefore, a new model was established by a backward stepwise process. The new model was constructed to rapidly discriminate benign from malignant pulmonary nodules, which could achieve accurate diagnosis of potential patients with lung cancer.     

Developing machine learning models to personalize care levels among emergency room patients for hospital admission

ObjectiveTo develop prediction models for intensive care unit (ICU) vs non-ICU level-of-care need within 24 hours of inpatient admission for emergency department (ED) patients using electronic health record data.Materials and MethodsUsing records of 41 654 ED visits to a tertiary academic center from 2015 to 2019, we tested 4 algorithms—feed-forward neural networks, regularized regression, random forests, and gradient-boosted trees—to predict ICU vs non-ICU level-of-care within 24 hours and at the 24th hour following admission. Simple-feature models included patient demographics, Emergency Severity Index (ESI), and vital sign summary. Complex-feature models added all vital signs, lab results, and counts of diagnosis, imaging, procedures, medications, and lab orders.ResultsThe best-performing model, a gradient-boosted tree using a full feature set, achieved an AUROC of 0.88 (95%CI: 0.87–0.89) and AUPRC of 0.65 (95%CI: 0.63–0.68) for predicting ICU care need within 24 hours of admission. The logistic regression model using ESI achieved an AUROC of 0.67 (95%CI: 0.65–0.70) and AUPRC of 0.37 (95%CI: 0.35–0.40). Using a discrimination threshold, such as 0.6, the positive predictive value, negative predictive value, sensitivity, and specificity were 85%, 89%, 30%, and 99%, respectively. Vital signs were the most important predictors.Discussion and ConclusionsUndertriaging admitted ED patients who subsequently require ICU care is common and associated with poorer outcomes. Machine learning models using readily available electronic health record data predict subsequent need for ICU admission with good discrimination, substantially better than the benchmarking ESI system. The results could be used in a multitiered clinical decision-support system to improve ED triage.     

Nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles

Background:Existing clinical prediction models for in vitro fertilization are based on the fresh oocyte cycle, and there is no prediction model to evaluate the probability of successful thawing of cryopreserved mature oocytes. This research aims to identify and study the characteristics of pre-oocyte-retrieval patients that can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles.Methods:Data were collected from the Reproductive Center, Peking University Third Hospital of China. Multivariable logistic regression model was used to derive the nomogram. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test and calibration plots.Results:The predictors in the model of “no transferable embryo cycles” are female age (odds ratio [OR] = 1.099, 95% confidence interval [CI] = 1.003–1.205, P = 0.0440), duration of infertility (OR = 1.140, 95% CI = 1.018–1.276, P = 0.0240), basal follicle-stimulating hormone (FSH) level (OR = 1.205, 95% CI = 1.051–1.382, P = 0.0084), basal estradiol (E2) level (OR = 1.006, 95% CI = 1.001–1.010, P = 0.0120), and sperm from microdissection testicular sperm extraction (MESA) (OR = 7.741, 95% CI = 2.905–20.632, P < 0.0010). Upon assessing predictive ability, the AUC for the “no transferable embryo cycles” model was 0.799 (95% CI: 0.722–0.875, P < 0.0010). The Hosmer–Lemeshow test (P = 0.7210) and calibration curve showed good calibration for the prediction of no transferable embryo cycles. The predictors in the cumulative live birth were the number of follicles on the day of human chorionic gonadotropin (hCG) administration (OR = 1.088, 95% CI = 1.030–1.149, P = 0.0020) and endometriosis (OR = 0.172, 95% CI = 0.035–0.853, P = 0.0310). The AUC for the “cumulative live birth” model was 0.724 (95% CI: 0.647–0.801, P < 0.0010). The Hosmer–Lemeshow test (P = 0.5620) and calibration curve showed good calibration for the prediction of cumulative live birth.Conclusions:The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, high basal FSH and E2 level, endometriosis, sperm from MESA, and low number of follicles with a diameter >10 mm on the day of hCG administration.     

慢性鼻-鼻窦炎伴鼻息肉术后复发的预测模型建立

目的 研究慢性鼻-鼻窦炎伴鼻息肉术后复发的预测模型建立。方法 选择2014年9月至2018年6月在南京大学医学院附属鼓楼医院高淳分院治疗的258例鼻-鼻窦炎伴鼻息肉患者作为模型建立组,建立术后复发的预测模型,选择同期本院收治的134例患者验证模型的区分度和校准度。结果 logistic回归分析结果显示,吸烟史（OR=2.998,95% CI：2.095~4.292）、鼻窦总积分（OR=1.489,95% CI：1.129~1.963）、支气管哮喘（OR=2.186,95% CI：1.688~2.831）、变应性鼻炎（OR=1.740,95% CI：1.403~2.159）、头/面疼痛评分（OR=2.083,95% CI：1.637~2.651）及嗅觉损伤评分（OR=1.879,95% CI：1.509~2.341）是患者术后复发的影响因素（P<0.05）。结论 吸烟史、鼻窦总积分、支气管哮喘、变应性鼻炎、头/面疼痛评分及嗅觉损伤评分等影响因素建立预测模型可有效预测患者术后复发。     

Bacteria profiles and risk factors for proven early-onset sepsis in preterm neonates

Objectives:To fill this gap in the literature and establish a prediction model.Methods:This retrospective study was conducted in a Neonatal Intensive Care Unit between April 2018 and April 2019. Sixty-five sepsis cases were clinically diagnosed with early-onset neonatal sepsis (EONS) and confirmed by bacterial culture, and 91 newborns were enrolled as control. Multivariate analysis was used to analyze the data, and the predictive efficiency of the prediction model was verified using the Hosmer-Lemeshow goodness-of-fit test and receiver operating characteristic (ROC) curve.Results:There were differences in multiple gestation birth, cesarean section, gestational age, birth weight, Apgar scores at 1 and 5 minutes, and laboratory parameters including white blood cells, neutrophils, lymphocytes, platelets, C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) between the groups. Multivariable logistic analysis showed that the Apgar score at 5 minutes and CRP, PCT, and IL-6 levels remained independent significant predictors of EONS (p=0.002, p=0.020, p=0.002, p=0.021, respectively). According to multivariate logistic regression analysis, the risk prediction model was logit p=24.280–3.270 x Apgar score at 5 minutes + 1.396 x CRP + 3.409 x PCT + 0.034 x IL-6 (Lemeshow goodness-of-fit test p=0.143). Receiver operating characteristic curve analysis indicated that the area under the curve was 0.968 (95% confidence interval: 0.936–1.000, p<0.05), and the sensitivity was 90.8% and specificity was 97.8%.Conclusion:The Apgar score at 5 minutes and CRP, PCT, and IL-6 levels are risk factors for EONS. The risk prediction model is highly efficient in predicting EONS among preterm infants.     

Risk factors associated with osteoporosis and fracture in psoriatic arthritis

Background:Although there are few studies mentioned there may be some relationship between psoriatic arthritis (PsA) and osteoporosis, clinical data in real world still need to be clarified in China. The aim of this study was to assess the areal and volumetric bone mineral density (BMD), frequency of fracture, and risk factors in patients with PsA.Methods:A total of one hundred PsA patients who visited Peking University First Hospital and one hundred age- and sex-matched healthy controls with DXA data were enrolled in the study. Patients with clinical fractures confirmed by X-ray during follow-up were also recorded. Clinical characteristics of the patients were recorded and compared between the abnormal BMD group and the normal BMD group, as well as between the fracture and non-fracture groups. Risk factors for fracture and low BMD were analyzed.Results:Mean BMD at the total hip and femoral neck was significantly lower in PsA patients than that in healthy controls (0.809 ± 0.193 vs. 0.901 ± 0.152 g/cm², P = 0.041; 0.780 ± 0.146 vs. 0.865 ± 0.166 g/cm², P = 0.037, respectively). Moreover, lumbar spine BMD was negatively correlated with psoriasis duration, swollen joint count and DAS28-CRP (r = –0.503, –0.580, –0.438; P < 0.05). Total hip BMD and femoral neck BMD were negatively correlated with HAQ (r = –0.521, –0.335; P < 0.05). Fractures occurred in 29 patients during the follow-up period. Logistic regression analysis showed that older age (OR 1.132 [95%CI: 1.026–1.248), P < 0.05], higher HAQ score (OR 1.493, 95%CI: 1.214–1.836, P < 0.01), higher disease activity index for psoriatic arthritis (OR 1.033, 95% CI: 1.002–1.679, P < 0.05) and hip joint involvement (OR 6.401, 95% CI: 4.012–44.180, P < 0.05) were risk factors for fracture in the multivariate model.Conclusions:Increased risks of osteoporosis and fracture were found in PsA patients compared to healthy controls. Besides age, high disease activity and hip joint involvement were risk factors for decreased BMD and fracture.     

Sharing patient-generated data with healthcare providers: findings from a 2019 national survey

ObjectiveOur study estimates the prevalence and predictors of wearable device adoption and data sharing with healthcare providers in a nationally representative sample.Materials and MethodsData were obtained from the 2019 Health Information National Trend Survey. We conducted multivariable logistic regression to examine predictors of device adoption and data sharing.ResultsThe sample contained 4159 individuals, 29.9% of whom had adopted a wearable device in 2019. Among adopters, 46.3% had shared data with their provider. Individuals with diabetes (odds ratio [OR], 2.39; 95% CI, 1.66–3.45; P < .0001), hypertension (OR, 2.80; 95% CI, 2.12–3.70; P < .0001), and multiple chronic conditions (OR, 1.55; 95% CI, 1.03–2.32; P < .0001) had significantly higher odds of wearable device adoption. Individuals with a usual source of care (OR, 2.44; 95% CI, 1.95–3.04; P < .0001), diabetes (OR, 1.66; 95% CI, 1.32–2.08; P < .0001), and hypertension (OR, 1.78; 95% CI, 1.44–2.20; P < .0001) had significantly higher odds of sharing data with providers.DiscussionA third of individuals adopted a wearable medical device and nearly 50% of individuals who owned a device shared data with a provider in 2019. Patients with certain conditions, such as diabetes and hypertension, were more likely to adopt devices and share data with providers. Social determinants of health, such as income and usual source of care, negatively affected wearable device adoption and data sharing, similarly to other consumer health technologies.ConclusionsWearable device adoption and data sharing with providers may be more common than prior studies have reported; however, digital disparities were noted. Studies are needed that test implementation strategies to expand wearable device use and data sharing into care delivery.     

多因素Logistic回归模型和XGBoost模型对舌癌患者放疗期间发生口腔感染的预测价值

目的 探讨多因素Logistic回归模型和XGBoost模型对舌癌患者放疗期间发生口腔感染的预测价值。方法 选取2003年1月—2022年12月在新疆医科大学第一附属医院就诊的431例放疗的舌癌患者,随机分为训练组288例和预测组143例。比较多因素Logistic回归模型和XGBoost模型对舌癌患者放疗期间发生口腔感染的预测效能。结果 多因素Logistic回归模型结果显示,年龄［O^R =3.250（95% CI：1.476,7.634）］,肿瘤分期［O^R =2.941（95% CI：1.248,7.613）］,口腔环境［O^R =0.210（95% CI：0.079,0.502）］,是否手术［O^R =0.285（95% CI：0.113,0.663）］,血红蛋白［O^R =0.323（95% CI：0.139,0.712）］,血清白蛋白［O^R =0.353（95% CI：0.148,0.851）］是放疗期间发生口腔感染的独立预测因素。XGBoost模型结果显示,口腔环境、手术、肿瘤分期、血清白蛋白、年龄、同步化疗、红细胞计数、血红蛋白、中性粒细胞计数为重要性指标。多因素Logistic回归模型和XGBoost模型受试者工作特征曲线下面积分别为0.830、0.835,两者比较,差异无统计学意义（P >0.05）;敏感性分别为88.24%（95% CI：0.729,1.000）、82.35%（95% CI：0.642,1.000）;特异性分别为68.25%（95% CI：0.601,0.764）、69.84%（95% CI：0.627,0.786）。结论 多因素Logistic回归模型和XGBoost模型对舌癌患者放疗期间发生口腔感染的预测均有意义,两者预测效能相当。建立模型有助于筛选出口腔感染的高危人群,及早采取预防措施,降低口腔感染发生风险。     

Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults

ObjectiveTo compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.Materials and MethodsWe evaluated models for stroke risk at varying intervals of follow-up (<9 years, 0–3 years, 3–6 years, 6–9 years) in 503 842 adults without prior history of stroke recruited from 10 areas in China in 2004–2008. Inputs included sociodemographic factors, diet, medical history, physical activity, and physical measurements. We compared discrimination and calibration of Cox regression, logistic regression, support vector machines, random survival forests, gradient boosted trees (GBT), and multilayer perceptrons, benchmarking performance against the 2017 Framingham Stroke Risk Profile. We then developed an ensemble approach to identify individuals at high risk of stroke (>10% predicted 9-yr stroke risk) by selectively applying either a GBT or Cox model based on individual-level characteristics.ResultsFor 9-yr stroke risk prediction, GBT provided the best discrimination (AUROC: 0.833 in men, 0.836 in women) and calibration, with consistent results in each interval of follow-up. The ensemble approach yielded incrementally higher accuracy (men: 76%, women: 80%), specificity (men: 76%, women: 81%), and positive predictive value (men: 26%, women: 24%) compared to any of the single-model approaches.Discussion and ConclusionAmong several approaches, an ensemble model combining both GBT and Cox models achieved the best performance for identifying individuals at high risk of stroke in a contemporary study of Chinese adults. The results highlight the potential value of expanding the use of ML in clinical practice.     

Validation of the kidney failure risk equation in predicting the risk of progression to kidney failure in a multi-ethnic Singapore chronic kidney disease cohort

INTRODUCTIONThe Kidney Failure Risk Equation (KFRE) was developed to predict the risk of progression to end-stage kidney disease (ESKD). Although the KFRE has been validated in multinational cohorts, the Southeast Asian population was under-represented. This study aimed to validate the KFRE in a multi-ethnic Singapore chronic kidney disease (CKD) cohort.METHODSStage 3–5 CKD patients referred to the renal medicine department at Singapore General Hospital in 2009 were included. The primary outcome (time to ESKD) was traced until 30 June 2017. The eight- and four-variable KFRE (non-North America) models using age, gender, estimated glomerular filtration rate, urine albumin-creatinine ratio, serum albumin, phosphate, bicarbonate and calcium were validated in our cohort. Cox regression, likelihood ratio (Χ²), adequacy index, Harrell’s C-index and calibration curves were calculated to assess the predictive performance, discrimination and calibration of these models on the cohort.RESULTSA total of 1,128 patients were included. During the study period, 252 (22.3%) patients reached ESKD at a median time to ESKD of 84.8 (range 0.1–104.7) months. Both the eight- and four-variable KFRE models showed excellent predictive performance and discrimination (eight-variable: C-index 0.872, 95% confidence interval [CI] 0.850–0.894, adequacy index 97.3%; four-variable: C-index 0.874, 95% CI 0.852–0.896, adequacy index 97.9%). There was no incremental improvement in the prediction ability of the eight-variable model over the four-variable model in this cohort.CONCLUSIONThe KFRE was validated in a multi-ethnic Singapore CKD cohort. This risk score may help to identify patients requiring early renal care.     

Risk factors associated with medication ordering errors

ObjectiveWe utilized a computerized order entry system–integrated function referred to as “void” to identify erroneous orders (ie, a “void” order). Using voided orders, we aimed to (1) identify the nature and characteristics of medication ordering errors, (2) investigate the risk factors associated with medication ordering errors, and (3) explore potential strategies to mitigate these risk factors.Materials and MethodsWe collected data on voided orders using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. Interviews were informed by the human factors–based SEIPS (Systems Engineering Initiative for Patient Safety) model to characterize the work systems–based risk factors contributing to ordering errors; chart reviews were used to establish whether a voided order was a true medication ordering error and ascertain its impact on patient safety.ResultsDuring the 16-month study period (August 25, 2017, to December 31, 2018), 1074 medication orders were voided; 842 voided orders were true medication errors (positive predictive value = 78.3 ± 1.2%). A total of 22% (n = 190) of the medication ordering errors reached the patient, with at least a single administration, without causing patient harm. Interviews were conducted on 355 voided orders (33% response). Errors were not uniquely associated with a single risk factor, but the causal contributors of medication ordering errors were multifactorial, arising from a combination of technological-, cognitive-, environmental-, social-, and organizational-level factors.ConclusionsThe void function offers a practical, standardized method to create a rich database of medication ordering errors. We highlight implications for utilizing the void function for future research, practice and learning opportunities.     

Prevalence and correlates of common mental disorders among children and adolescents in Blantyre-Urban,Malawi

BackgroundThe high global prevalence of mental disorders justifies the need to quantify their burden in the sub-Saharan Africa where there is a dearth of information. These mental disorders are linked to different socio-demographic factors.ObjectiveTo determine the prevalence of, and factors associated with mental disorders among children and adolescents in Blantyre City, Malawi.MethodsChildren and adolescents aged 6 to 17 years were interviewed to determine their socio-demographic characteristics and assess their mental health status using the Strengths and Difficulties Questionnaire (SDQ) and Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS). Associations between mental disorders and socio-demographic characteristics were tested using Chi-square and logistic regression analysis.ResultsThe prevalence of symptoms of psychopathology on the SDQ was 7.3% (95%CI 4.8–10.5%) while for the K-SADS was 5.9% (95% CI 3.7%–8.9%). The prevalence of mental disorders across the age ranges of 6 to 12 years and 13 to 17 years was 5.4% and 7.9 % respectively. Males had a higher prevalence (7.1%) compared to females (4.7%). Conduct disorder was most prevalent (3.4%), followed by either type of ADHD-Attention Deficit Hyperactive Disorders (2.0%). Having a single parent (p<0.001), staying with a non-biological guardian (p<0.030), engaging in paid work (p<0.039), not attending school (p<0.019) and having teacher difficulties(p<0.028) were positively associated with a mental disorder.ConclusionThe socio-demographic factors associated with the risk of developing mental disorders may be important targets for mental health intervention programs.     

重症COVID-19预测模型的建立和评价

目的 建立重症新型冠状病毒性肺炎(COVID-19)的预测模型,并对模型进行评价.方法 回顾性分析2020年1月1日至3月10日于江汉大学附属湖北省第三人民医院确诊的314例COVID-19患者入院时的基线资料,按住院后是否进展为重症COVID-19分为重症组(76例)和非重症组(238例),将两组患者间差异显著的指标纳入logistic回归分析得出重症COVID-19的独立危险因素,将所有独立危险因素的回归系数代入方程,建立一个新的联合预测因子(L)模型,以预测重症COVID-19的发生风险.结果 logistic回归分析结果显示,年龄增大(OR=1.138,95％CI:1.080～1.199)、身体质量指数增高(OR=2.346,95％CI:1.509～3.646)、白细胞计数降低(OR=0.519,95％CI:0.357～0.754)、血浆白蛋白水平降低(OR=0.692,95％CI:0.588～0.815)、C反应蛋白水平升高(OR=1.029,95％CI:1.007～1.050)、D-二聚体水平升高(OR=1.278,95％CI:1.089～1.499)是患者发生重症COVID-19的独立危险因素.将这6个独立危险因素拟合为一个新变量,即为联合预测因子L,L的受试者工作特征(ROC)曲线下面积(AUC)为0.985(95％CI:0.974～0.996),截断点为12.90,敏感度为93.4％,特异度为95.0％.结论 联合预测因子在各变量中对于重症COVID-19发生的预测价值最高,这对重症患者的早期识别有一定的临床指导意义.     

Association between age at arrival,duration of migration,and overweight/obesity in Chinese rural-to-urban migrants: the Yi migrant study

Background:Urbanization in China is rapidly proceeding, but rural-to-urban migration and its association with overweight and obesity is not well studied. This study aimed to explore the age at arrival, duration of migration, and the corresponding association with overweight/obesity in Yi migrants in China.Methods:A cross-sectional study was conducted in rural and urban areas in 2015 in Sichuan province, China. Demographic characteristics, lifestyle factors, and anthropometry were collected. General linear regression models were used to assess the effect of duration of migration (1–10, 11–20, 21–30, and >30 years) on body mass index (BMI). Multi-variable logistic regression was used to examine the association between duration of migration and overweight/obesity (BMI ≥ 25 kg/m²).Results:A total of 3056 Yi people (1894 Yi farmers and 1162 Yi migrants) aged 20 to 80 years were enrolled. After adjusting for age, sex, and other potential confounders, Yi migrants had 1.71 kg/m² (95% confidence interval [CI]: 1.36–2.06) higher BMI and a 2.13-fold (95% CI: 1.71–2.65) higher risk of overweight/obesity than Yi farmers. In Yi migrants, stratified by age at arrival, no significant association between duration of migration and overweight/obesity was observed in those who were 0 to 20 years old at arrival. In comparison, in migrants >20 years old at arrival, compared with the reference group (1–10 years), long-term migration (>30 years) was found to be associated with overweight/obesity after adjustment (odds ratio: 1.85, 95% CI: 1.04–3.29).Conclusions:Yi migrants were observed to have greater risk of overweight/obesity than Yi farmers. In Yi migrants, the risk of overweight/obesity increased according to the duration of migration, especially in those who were older upon their arrival.     

Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review

ObjectivesThis systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators.Materials and MethodsWe searched Embase, Medline, Web-of-Science, Cochrane Library, and Google Scholar to identify studies that developed 1 or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009–2019.ResultsWe identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009–2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented.DiscussionOverall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented.ConclusionImprovement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.     

甲状腺乳头状癌侧颈淋巴结转移的危险因素分析及预测模型构建

目的 探讨甲状腺乳头状癌侧颈区淋巴结转移的危险因素并构建临床预测模型。方法 回顾性分析2019年1月至2019年12月就诊于安徽医科大学第二附属医院的230例甲状腺乳头状癌患者的临床资料,根据是否存在侧颈区淋巴结转移将患者分为转移组(n=39例)和非转移组(n=191例)。比较两组患者的性别、年龄、单/双侧、肿瘤最大径、是否多灶、有无合并桥本甲状腺炎、是否紧贴包膜、有无钙化等指标的差异,对差异有统计学意义的指标应用多因素logistic回归(逐步回归)分析侧颈区淋巴结转移的独立危险因素。通过R软件进行临床建模并绘制列线图,并计算C(校正)指数、绘制校准曲线、受试者特征(ROC)曲线进一步评估预测模型的效能。结果 两组患者性别、单/双侧、肿瘤最大径、是否多灶、是否紧贴包膜、有无钙化比较,差异有统计学意义(P＜0.05);多因素logistic逐步回归分析结果显示,性别、单/双侧、肿瘤最大径为侧颈区淋巴结转移的独立危险因素。根据上述3个因素构建诺莫预测模型,模型预测甲状腺乳头状癌患者发生侧颈转移的ROC曲线下面积为0.82(95%CI:0.76 ~0.89);构建模型的C-index为0.82(95%CI:0.76 ~ 0.88),校正C-index为0.80;模型的校准曲线与实际曲线重合较好。结论 性别、单/双侧癌、肿瘤最大径是甲状腺乳头状癌侧颈区淋巴结转移的独立危险因素;基于上述因素进行的临床建模对甲状腺乳头状癌患者发生侧颈区淋巴结转移的风险预测具有一定的价值。     

Long-term results of extensive aortoiliac occlusive disease (EAIOD) treated by endovascular therapy and risk factors for loss of primary patency

Background:Although endovascular therapy has been widely used for focal aortoiliac occlusive disease (AIOD), its performance for extensive AIOD (EAIOD) is not fully evaluated. We aimed to demonstrate the long-term results of EAIOD treated by endovascular therapy and to identify the potential risk factors for the loss of primary patency.Methods:Between January 2008 and June 2018, patients with a clinical diagnosis of the 2007 TransAtlantic Inter-Society Consensus II (TASC II) C and D AIOD lesions who underwent endovascular treatment in our institution were enrolled. Demographic, diagnosis, procedure characteristics, and follow-up information were reviewed. Univariate analysis was used to identify the correlation between the variables and the primary patency. A multivariate logistic regression model was used to identify the independent risk factors associated with primary patency. Five- and 10-year primary and secondary patency, as well as survival rates, were calculated by Kaplan-Meier analysis.Results:A total of 148 patients underwent endovascular treatment in our center. Of these, 39.2% were classified as having TASC II C lesions and 60.8% as having TASC II D lesions. The technical success rate was 88.5%. The mean follow-up time was 79.2 ± 29.2 months. Primary and secondary patency was 82.1% and 89.4% at 5 years, and 74.8% and 83.1% at 10 years, respectively. The 5-year survival rate was 84.2%. Compared with patients without loss of primary patency, patients with this condition showed significant differences in age, TASC II classification, infrainguinal lesions, critical limb ischemia (CLI), and smoking. Multivariate logistic regression analysis showed age <61 years (adjusted odds ratio [aOR]: 6.47; 95% CI: 1.47–28.36; P = 0.01), CLI (aOR: 7.81; 95% CI: 1.92–31.89; P = 0.04), and smoking (aOR: 10.15; 95% CI: 2.79–36.90; P < 0.01) were independent risk factors for the loss of primary patency.Conclusion:Endovascular therapy was an effective treatment for EAIOD with encouraging patency and survival rate. Age <61 years, CLI, and smoking were independent risk factors for the loss of primary patency.     

Anemia and insomnia: a cross-sectional study and meta-analysis

BackgroundSeveral recent genome-wide association studies suggested insomnia and anemia may share some common genetic components. We thus examined whether adults with anemia had higher odds of having insomnia relative to those without anemia in a cross-sectional study and a meta-analysis.MethodsIncluded in this cross-sectional study were 12,614 Chinese adults who participated in an ongoing cohort, the Kailuan Study. Anemia was defined as hemoglobin levels below 12.0 g/dL in women and 13.0 g/dL in men. Insomnia was assessed using the Chinese version of the Athens Insomnia Scale (AIS). A total AIS score ≥6 was considered insomnia. The association between anemia and insomnia was assessed using a logistic regression model, adjusting for potential confounders such as age, sex, chronic disease status, and plasma C-reactive protein concentrations. A meta-analysis was conducted using the fixed effects model to pool results from our study and three previously published cross-sectional studies on this topic in adult populations.ResultsIndividuals with anemia had greater odds of having insomnia (adjusted odds ratio [OR]: 1.32; 95% confidence interval [CI]: 1.03–1.70) compared with individuals without anemia. A significant association persisted after we excluded individuals with chronic inflammation, as suggested by C-reactive protein levels >1 mg/L (adjusted OR: 1.68; 95% CI: 1.22–2.32). The meta-analysis results, including 22,134 participants, also identified a positive association between anemia and insomnia (pooled OR: 1.39; 95% CI: 1.22–1.57).ConclusionsThe presence of anemia was significantly associated with a higher likelihood of having insomnia in adults. Due to the nature of the cross-sectional study design, results should be interpreted with caution.     

Immunogenicity and safety of a recombinant fusion protein vaccine (V-01) against coronavirus disease 2019 in healthy adults: a randomized,double-blind,placebo-controlled,phase II trial

Background:Innovative coronavirus disease 2019 (COVID-19) vaccines, with elevated global manufacturing capacity, enhanced safety and efficacy, simplified dosing regimens, and distribution that is less cold chain-dependent, are still global imperatives for tackling the ongoing pandemic. A previous phase I trial indicated that the recombinant COVID-19 vaccine (V-01), which contains a fusion protein (IFN-PADRE-RBD-Fc dimer) as its antigen, is safe and well tolerated, capable of inducing rapid and robust immune responses, and warranted further testing in additional clinical trials. Herein, we aimed to assess the immunogenicity and safety of V-01, providing rationales of appropriate dose regimen for further efficacy study.Methods:A randomized, double-blind, placebo-controlled phase II clinical trial was initiated at the Gaozhou Municipal Centre for Disease Control and Prevention (Guangdong, China) in March 2021. Both younger (n = 440; 18–59 years of age) and older (n = 440; ≥60 years of age) adult participants in this trial were sequentially recruited into two distinct groups: two-dose regimen group in which participants were randomized either to follow a 10 or 25 μg of V-01 or placebo given intramuscularly 21 days apart (allocation ratio, 3:3:1, n = 120, 120, 40 for each regimen, respectively), or one-dose regimen groups in which participants were randomized either to receive a single injection of 50 μg of V-01 or placebo (allocation ratio, 3:1, n = 120, 40, respectively). The primary immunogenicity endpoints were the geometric mean titers of neutralizing antibodies against live severe acute respiratory syndrome coronavirus 2, and specific binding antibodies to the receptor binding domain (RBD). The primary safety endpoint evaluation was the frequencies and percentages of overall adverse events (AEs) within 30 days after full immunization.Results:V-01 provoked substantial immune responses in the two-dose group, achieving encouragingly high titers of neutralizing antibody and anti-RBD immunoglobulin, which peaked at day 35 (161.9 [95% confidence interval [CI]: 133.3–196.7] and 149.3 [95%CI: 123.9–179.9] in 10 and 25 μg V-01 group of younger adults, respectively; 111.6 [95%CI: 89.6–139.1] and 111.1 [95%CI: 89.2–138.4] in 10 and 25 μg V-01 group of older adults, respectively), and remained high at day 49 after a day-21 second dose; these levels significantly exceed those in convalescent serum from symptomatic COVID-19 patients (53.6, 95%CI: 31.3–91.7). Our preliminary data show that V-01 is safe and well tolerated, with reactogenicity predominantly being absent or mild in severity and only one vaccine-related grade 3 or worse AE being observed within 30 days. The older adult participants demonstrated a more favorable safety profile compared with those in the younger adult group: with AEs percentages of 19.2%, 25.8%, 17.5% in older adults vs. 34.2%, 23.3%, 26.7% in younger adults at the 10, 25 μg V-01 two-dose group, and 50 μg V-01 one-dose group, respectively.Conclusions:The vaccine candidate V-01 appears to be safe and immunogenic. The preliminary findings support the advancement of the two-dose, 10 μg V-01 regimen to a phase III trial for a large-scale population-based evaluation of safety and efficacy.Trial Registration:http://www.chictr.org.cn/index.aspx (No. ChiCTR2100045107, http://www.chictr.org.cn/showproj.aspx?proj=124702).