妊娠期高血压患者妊娠结局调查及相关影响因素分析

目的 调查温州市人民医院2018—2021年妊娠期高血压疾病(hypertensive disorders of pregnancy,HDP)患者妊娠结局,并分析其妊娠结局的相关影响因素,为临床采取对应干预措施、降低不良妊娠结局发生风险提供参考。 方法 选取2018年1月—2021年12月于温州市人民医院分娩的400例HDP患者(单胎妊娠)的临床资料,开展回顾性分析,根据其妊娠结局分为不良妊娠结局组(n=157)与正常妊娠结局组(n=243)。比较两组临床资料,分析HDP患者不良妊娠结局发生的影响因素,构建logistic回归模型方程,并分析logistic回归模型的预测价值。 结果 400例HDP患者中共157例(39.25%)发生不良妊娠结局;单因素分析显示患者年龄、孕前BMI、分娩方式、妊娠期糖尿病(gestational diabetes mellitus,GDM)、负性情绪与不良妊娠结局的发生有关(P＜0.05);logistic回归模型显示,年龄≥35岁(OR=23.815,95%CI:10.370～54.655)、孕前BMI≥24.0(OR=16.010,95%CI:6.832～34.620)、阴道分娩(OR=16.336,95%CI:7.325～36.403)、GDM(OR=26.337,95%CI:11.908～58.253)、负性情绪(OR=20.682,95%CI:2.791～54.876)均为HDP患者不良妊娠结局发生的独立危险因素(P＜0.05);5个独立危险因素构建logistic回归模型方程为logistic(P)=-4.125+年龄×3.170+孕前BMI×2.773+阴道分娩×2.793+GDM×3.271+负性情绪×3.029;当logistic(P)=4.11,预测HDP患者发生不良妊娠结局的曲线下面积为0.899(95%CI:0.865～0.926),预测敏感度为84.36%,特异度为82.17%;根据设定的评分标准与不良妊娠结局发生情况,可将HDP患者划分为低风险(0～4分)、中风险(5～8分)与高风险(9～12分)。 结论 HDP患者不良妊娠结局发生率较高,年龄、孕前BMI、阴道分娩、GDM、负性情绪均为不良妊娠结局发生的影响因素,构建logistic回归模型可预测不良妊娠结局发生风险,有助于临床制定相关干预措施。     

长沙体检人群身体质量指数与脂肪肝关系的探讨

目的 分析身体质量指数(body mass index, BMI)与脂肪肝的关系,明确发生脂肪肝的BMI临界值,以便及时识别并干预脂肪肝高危人群。 方法 收集2020年1月—2021年8月在湖南省人民医院健康管理中心进行体检的成年人健康体检资料24 019例,根据我国目前超重和肥胖标准分成体重过轻组、正常组、超重组和肥胖组,使用二元logistic 回归分析评估四组患脂肪肝的风险,利用受试者工作特征曲线(receiver operator characteristic curve, ROC曲线)判断发生脂肪肝的BMI临界值。 结果 ①本次所选取长沙地区体检人群的脂肪肝患病率为35.64%;②二元logistic回归分析矫正混杂因素后显示超重组和肥胖组患脂肪肝的风险分别是正常组的2.946倍和9.168倍(P<0.05),表明BMI增高是脂肪肝的独立危险因素;③超重组和肥胖组患脂肪肝的风险在不同的甘油三酯和高密度脂蛋白胆固醇水平中存在差异(P_交互作用<0.05)。④ROC曲线显示,男性发生脂肪肝的BMI 临界值为24.60,曲线下面积(area under curve, AUC)为0.814,女性发生脂肪肝的BMI临界值为23.20,AUC为0.848。 结论 BMI增高是脂肪肝的独立危险因素,对脂肪肝有一定的预测价值,当男性BMI＞24.60、女性BMI＞23.20时,应重视体重管理,以减少脂肪肝的发生。     

我国城乡基层“2 + X”家庭医生团队现状与预测研究

目的 预测“十四五”期间我国城乡人口和基层医疗卫生机构“2 + X”家庭医生团队数量,探究我国城乡家庭医生服务覆盖情况,为家庭医生健康发展提供科学依据。方法 依据2013—2019年相关数据,采用GM(1,1)灰色预测模型预测2020—2025年我国城乡人口数和“2 + X”家庭医生团队数量和相关卫技人员数。观察每 2000 服务人口标准下,2018—2025年我国城乡“2 + X”家庭医生团队服务人数和服务覆盖率。结果 2020—2025年我国城镇人口数逐年增长,到2025年增至98 831万人;城市基层医疗卫生机构全科医生数、注册护士数、家庭医生团队数量逐年增长,到2025年分别增至171 712人、848 324人、171 712个;全科医生与护士比逐年上升,到2025年升至1∶4.94;2018—2025年我国城市“2 + X”家庭医生团队服务人口覆盖率逐年上升,到2025年升至35%。2020—2025年我国乡村人口数逐年下降,到2025年降至48 053万人;乡镇基层医疗卫生机构全科医生数、注册护士数、家庭医生团队数量逐年增长,到2025年分别增至447 672人、854 976人、447 672个;全科医生与护士比逐年下降,到2025年降至1:1.91;2018—2025年我国乡村“2 + X”家庭医生团队服务人口覆盖率逐年上升,理论上2022年实现全面覆盖。结论 2018—2025年我国城乡“2 + X”家庭医生团队数量逐年增加,但城市家庭医生缺口较大,应加强全科医学人才培养,增强家庭医生职业吸引力,缩小城乡全科医生薪资差距,优化家庭医生服务覆盖均衡性。     

The Role of Maternal Weight in the Hierarchy of Macrosomia Predictors; Overall Effect of Analysis of Three Prediction Indicators

So far it has not been established which maternal features play the most important role in newborn macrosomia. The aim of this study is to provide assessment of a hierarchy of twenty six (26) maternal characteristics in macrosomia prediction. A Polish prospective cohort of women with singleton pregnancy (N = 912) which was recruited in the years 2015–2016 has been studied. Two analyses were performed: for probability of macrosomia > 4000 g (n = 97) (vs. 755 newborns 2500–4000 g); and for birthweight > 90th percentile (n = 99) (vs. 741 newborns 10–90th percentile). A multiple logistic regression was used (with 95% confidence intervals (CI)). A hierarchy of significance of potential predictors was established after summing up of three prediction indicators (NRI, IDI and AUC) calculated for the basic prediction model (maternal age + parity) extended with one (test) predictor. ‘Net reclassification improvement’ (NRI) focuses on the reclassification table describing the number of women in whom an upward or downward shift in the disease probability value occurred after a new factor had been added, including the results for healthy and ill women. ‘Integrated discrimination improvement’ (IDI) shows the difference between the value of mean change in predicted probability between the group of ill and healthy women when a new factor is added to the model. The area under curve (AUC) is a commonly used indicator. Results. The macrosomia risk was the highest for prior macrosomia (AOR = 7.53, 95%CI: 3.15–18.00, p < 0.001). A few maternal characteristics were associated with more than three times higher macrosomia odds ratios, e.g., maternal obesity and gestational age ≥ 38 weeks. A different hierarchy was shown by the prediction study. Compared to the basic prediction model (AUC = 0.564 (0.501–0.627), p = 0.04), AUC increased most when pre-pregnancy weight (kg) was added to the base model (AUC = 0.706 (0.649–0.764), p < 0.001). The values of IDI and NRI were also the highest for the model with maternal weight (IDI = 0.061 (0.039–0.083), p < 0.001), and (NRI = 0.538 (0.33–0.746), p < 0.001). Adding another factor to the base model was connected with significantly weaker prediction, e.g., for gestational age ≥ 38 weeks (AUC = 0.602 (0.543–0.662), p = 0.001), (IDI = 0.009 (0.004; 0.013), p < 0.001), and (NRI = 0.155 (0.073; 0.237), p < 0.001). After summing up the effects of NRI, IDI and AUC, the probability of macrosomia was most strongly improved (in order) by: pre-pregnancy weight, body mass index (BMI), excessive gestational weight gain (GWG) and BMI ≥ 25 kg/m². Maternal height, prior macrosomia, fetal sex-son, and gestational diabetes mellitus (GDM) occupied an intermediate place in the hierarchy. The main conclusions: newer prediction indicators showed that (among 26 features) excessive pre-pregnancy weight/BMI and excessive GWG played a much more important role in macrosomia prediction than other maternal characteristics. These indicators more strongly highlighted the differences between predictors than the results of commonly used odds ratios.     

基于季节性ARIMA模型的新疆肺结核发病预测分析

目的 探讨季节性时间序列模型(autoregressive integrated moving average,ARIMA)在新疆肺结核发病预测中的应用,并验证模型的可行性和适用性。 方法 采用季节性ARIMA(p, d, q )(P, D, Q)_s拟合2005年1月—2019年8月新疆地区肺结核月发病人数,建立多个季节时间序列模型并进行比较,选出最优模型对2019年9—12月肺结核发病人数进行预测。 结果 2005年1月—2019年8月新疆地区肺结核累积发病人数为627 869例,年平均发病人数为3 567例。 新疆地区肺结核月发病数具有季节性,1—5月平均发病数高于平均水平,6—12月平均发病数低于平均水平,发病高峰为1月和3月,发病低谷为9月。通过赤池信息量(Akaike Information Criterion,AIC)和贝叶斯信息量(Bayesian Information Criterion,BIC)最小原则得出,ARIMA(1, 1, 1 )(0, 1, 2)₁₂是最优模型,其残差序列为白噪声,参数的回归系数均具有统计学意义,拟合的平均绝对百分比误差MAPE为8.723%。预测的MAPE为18.674%,真实值均处于预测值的95%置信区间内。 结论 ARIMA(1, 1, 1 )(0, 1, 2)₁₂模型能够较好地拟合新疆肺结核发病数据,并进行短期预测,对新疆卫生防控措施的制定具有一定指导意义。     

我国常见细菌耐药趋势预测研究:基于灰色GM (1,1)模型

 目的 通过对耐甲氧西林金黄色葡萄球菌（MRSA）、耐碳青霉烯类铜绿假单胞菌（CRPA）、耐碳青霉烯类鲍曼不动杆菌（CRAB）、耐第三代头孢菌素的大肠埃希菌（3GCR-E.coli）、耐第三代头孢菌素的肺炎克雷伯菌（3GCR-KP）等细菌耐药数据构建灰色预测模型,分析细菌耐药特征的变化趋势,探讨灰色预测模型在细菌耐药领域的应用价值。方法 采用2014-2018年全国细菌耐药监测报告中MRSA、CRPA和CRAB、3GCR-E.coli、3GCR-KP等耐药率数据构建灰色预测GM （1,1）模型。用后验差比C值和小误差概率P值评估模型精度,用相对误差和级比偏差评估模型拟合效果,并用2019-2020年数据对模型预测效果进行验证。最终根据模型对2021-2023年的耐药率进行预测。结果 本研究构建的GM （1,1）模型对MRSA、CRPA、CRAB、3GCR-E.coli和3GCR-KP等细菌耐药率预测效果较好,根据该模型预测到2023年其耐药率分别可降低至23.9%、15.2%、50.2%、43.8%、26.1%。结论 全国针对细菌耐药情况采取的控制措施取得明显成效,GM （1,1）模型对细菌耐药率预测效果较好,可在细菌耐药管理领域推广应用。     

预测加氢裂化过程的产率

以缓和加氢裂化数据为基础,对于两种典型的加氢裂化动力学模型－－Stangeland模型和改进MHC模型,使用Shor最优化法进行了参数的拟合,比较了这两种动力学模型的结果、算法、复杂度以及预测能力。结果表明,改进MHC模型是一种更为合理的动力学模型,该模型也可用于实际加氢过程。     

Sperm binding to hyaluronan is an excellent predictor of Nili-Ravi buffalo bull fertility

This study reports the first evaluation of sperm hyaluronan binding assay (HBA) for predicting the fertility of Nili-Ravi buffalo bulls in relation to standard parameters of sperm quality. Cryopreserved semen doses of low (n = 6), medium (n = 3) and high fertility (n = 8) bulls based on their respective return rates were used. Significantly, more spermatozoa bound to hyaluronan from the most fertile bulls (57.15% ± 1.44) compared with medium (42.46% ± 1.08) and low fertility bulls (29.70% ± 0.78). A strongly positive correlation (r = .824, p < .01) was found between HBA and fertility that predicts a 67.9% variability (r² = .679, p < .01) in fertility. HBA was also strongly positively correlated with sperm viability (r = .679, p < .01) followed by their live/dead ratio (r = .637, p < .01), uncapacitated spermatozoa (r = .631, p < .01), normal apical ridge (r = .459, p < .01), motility (r = .434, p < .01), mature spermatozoa with low residual histones (r = .364, p < .01), high plasma membrane integrity (r = .316, p < .01) and nonfragmented DNA levels (r = .236, p < .05). It was negatively correlated with spermatozoa having reacted acrosome (r = −.654, p < .01). A fertility model built using a combination of sperm HBA and either sperm livability or viability predicts, respectively, 86.1% (r² = .861, p < .01) and 85.9% (r² = .859, p < .01) variability in buffalo bull fertility. In conclusion, sperm HBA may prove to be a single robust predictor of Nili-Ravi buffalo bull fertility.     

Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?

BackgroundApproximately 15%-20% of total knee arthroplasty (TKA) patients do not experience clinically meaningful improvements. We sought to compare the accuracy and parsimony of several machine learning strategies for developing predictive models of failing to experience minimal clinically important differences in patient-reported outcome measures (PROMs) 1 year after TKA.MethodsPatients (N = 587) in 3 large Veteran Health Administration facilities completed PROMs before and 1 year after TKA (92% follow-up). Preoperative PROMs and electronic health record data were used to develop and validate models to predict failing to experience at least a minimal clinically important difference in Knee Injury and Osteoarthritis Outcome Score (KOOS) Total, KOOS JR, and KOOS subscales (Pain, Symptoms, Activities of Daily Living, Quality of Life, and recreation). Several machine learning strategies were used for model development. Ten-fold cross-validation and bootstrapping were used to produce measures of overall accuracy (C-statistic, Brier Score). The sensitivity and specificity of various predicted probability cut-points were examined.ResultsThe most accurate models produced were for the Activities of Daily Living, Pain, Symptoms, and Quality of Life subscales of the KOOS (C-statistics 0.76, 0.72, 0.72, and 0.71, respectively). Strategies varied substantially in terms of the numbers of inputs required to achieve similar accuracy, with none being superior for all outcomes.ConclusionModels produced in this project provide estimates of patient-specific improvements in major outcomes 1 year after TKA. Integrating these models into clinical decision support, informed consent and shared decision making could improve patient selection, education, and satisfaction.Level of EvidenceLevel III, diagnostic study.     

Is Patient Satisfaction After Total Knee Arthroplasty Predictable Using Patient Characteristics and Preoperative Patient-Reported Outcomes?

BackgroundDissatisfaction after total knee arthroplasty (TKA) remains a difficult problem. Patient characteristics and preoperative patient-reported outcomes (PROs) are potential predictors of satisfaction one year after TKA. Being able to predict the outcome preoperatively might reduce the number of less satisfied patients.MethodsA retrospective cohort study on prospectively collected data of 1239 primary TKA patients (ASA I-II, BMI <35) was performed. Primary outcome was degree of patient satisfaction one year after TKA (Numeric Rating Scale (NRS) 0-10). Secondary outcomes were degree of patient satisfaction six months and two years after TKA and being dissatisfied (NRS 0-6) or satisfied (NRS 7-10) at all three time points. Multivariate linear and binary logistic regression analyses were executed with patient characteristics and preoperative PROs as potential predictors.ResultsOne year after TKA, median NRS satisfaction score was 9.0 (8.0-10.0) and 1117 (90.2%) patients were satisfied. BMI, degree of medial cartilage damage, previous knee surgery, Knee injury and Osteoarthritis Outcome Score-Physical Function Short Form score, EQ VAS score, and anxiety were identified as predictors of the degree of patient satisfaction (P = .000, R² = 0.027). Models on secondary outcomes reported R² of 1.7%-7.1% (P < .05). All models showed bad agreement between observed and predicted values for lower NRS satisfaction scores and being dissatisfied.ConclusionThe degree of patient satisfaction and the chance of being dissatisfied or satisfied six months, one, and two years after TKA are predictable by patient characteristics and preoperative PROs but not at a reliability level that is clinically useful.