The efficacy of expanded non‐invasive prenatal testing (NIPT) in a high‐risk twin pregnancies cohort by Meng Meng, Jianping Chen, Yingjun Yang, Yun Zhang, Gang Zou, Fenhe Zhou, Xing Wei, Yuchun Ge, Jia Zhou, Luming Sun

“…The sensitivity, specificity, positive predictive value, and negative predictive value of expanded NIPT were calculated. …”
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Motivos para practicar actividad física en estudiantes universitarios ecuatorianos (Reasons to practice physical activity in ecuadorian university students) by Borys León-Reyes, León Arguello Nuñez, Yosbanys Roque Herrera, Mauricio Cresp-Barria, Josivaldo Souza-Lima, Dilan Alberto Galeano Rojas

“…Finally, in the regression analysis, sex presented a significant predictive value in the motives analyzed. Therefore, the interests of Ecuadorian students are revealed, and key information is obtained to improve and increase the levels and adherence to PA in order to contribute to a more physically active life in the university population. …”
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Evaluating cut‐off levels for progesterone, β human chorionic gonadotropin and β human chorionic gonadotropin ratio to exclude pregnancy viability in women with a pregnancy of unkn... by Shabnam Bobdiwala, Christopher Kyriacou, Evangelia Christodoulou, Jessica Farren, Nicola Mitchell‐Jones, Maya Al‐Memar, Francis Ayim, Baljinder Chohan, Emma Kirk, Osama Abughazza, Bramara Guruwadahyarhalli, Sharmistha Guha, Veluppillai Vathanan, Debbie Gould, Catriona Stalder, Dirk Timmerman, Ben Van Calster, Tom Bourne

“…We evaluate the predictive value of progesterone, βhCG, and βhCG ratio cut‐off levels to exclude a VIUP in women with a pregnancy of unknown location. …”
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Endoplasmic reticulum stress-related prognosis signature characterizes the immune landscape and predicts the prognosis of colon adenocarcinoma by Lichao Cao, Lichao Cao, Lichao Cao, Lichao Cao, Haoyang Dai, Haoyang Dai, Shangqing Wei, Shangqing Wei, Ying Ba, Ying Ba, Ying Ba, Fang Chen, Fang Chen, Fang Chen, Yingying Chen, Yingying Chen, Yingying Chen, Chendi Yu, Chendi Yu, Chendi Yu, Shenrui Zhang, Shenrui Zhang, Shenrui Zhang, Erfei Chen, Erfei Chen, Erfei Chen, Hezi Zhang, Hezi Zhang, Hezi Zhang

“…The immunotherapeutic cohort was used to evaluate the predictive value of the ERSG signature for immunotherapeutic sensitivity.ResultsThe ERSG signature, consisted of HSPA1A, SERPINA1, and DAPK1, could predict the prognosis of patients with COAD. …”
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Analysis of risk factors for sepsis-related liver injury and construction of a prediction model by Yong He, Yong He, Yong He, Chi Wang, Chi Wang, Chi Wang, Wan He, Wan He, Wan He, He Zhang, He Zhang, He Zhang, Fei Ding, Fei Ding, Fei Ding, Ying Liu, Ying Liu, Ying Liu, He He, He He, He He, Binwu Ying, Binwu Ying, Binwu Ying, Xin Nie, Xin Nie, Xin Nie, Xin Nie

“…However, there is no clear diagnostic standard for SRLI, making early detection and intervention challenging.ObjectiveThis study aimed to investigate the predictive value of serum indices for the occurrence of SRLI in adults to guide clinical practice.MethodsIn this study, we investigated the predictive value of serum indices for SRLI in adults. …”
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Extracellular mRNA detected by molecular beacons in tethered lipoplex nanoparticles for diagnosis of human hepatocellular carcinoma. by Xinmei Wang, Kwang Joo Kwak, Zhaogang Yang, Aili Zhang, Xiaoli Zhang, Rachael Sullivan, Dan Lin, Robert L Lee, Carlos Castro, Kalpana Ghoshal, Carl Schmidt, L James Lee

“…This 2-mRNA combination also provided a perfect positive predictive value (PPV = 1) at a negative predictive value (NPV) of 0.95 and 20% prevalence, while the blood AFP protein or plasma EV GPC3 mRNA alone could only provide a PPV of 0.61 and 0.79 respectively at the same conditions. …”
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Predicting isolated impaired glucose tolerance without oral glucose tolerance test using machine learning in Chinese Han men by Lin Wang, Jing Xie, Zhaoyan Gu, Xinyu Miao, Lichao Ma, Shuangtong Yan, Yanping Gong, Chunlin Li, Banruo Sun, Yue Ruan

“…Model performance was evaluated using metrics including accuracy, recall, F1 score, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic curve (AUC). …”
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A correlation study between blood glucose fluctuation and chronic pain in the older people with type 2 diabetes mellitus by Qinyi Huang, Mengjie Cui, Yangliu Yin, Cancan Hui, Yuwei Cheng, Shuqian Wang, Xiujuan Hu, Xiaorong Yin, Xiaoxiang Zhang, Xinyu Sun, Tiantian Tang, Lang Lang, Yan Sun

“…Abstract Objectives To investigate the correlation between blood glucose fluctuation parameters and other clinical data with chronic pain in older patients ( ≧ 60 years) with type 2 diabetes mellitus (T2DM), as well as evaluating the predictive value of risk of these parameters for chronic pain. …”
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Potential of Urinary Mutation and Methylation Biomarkers in Selecting Candidates for Repeat Transurethral Resection of Bladder Tumor in Non–muscle-invasive Bladder Cancer: A Prospe... by Xiao Yang, Lingkai Cai, Yaqun Xin, Xinfeng Chen, Bing Zheng, Jie Han, Lin Yuan, Xuejian Yang, Pengchao Li, Juntao Zhuang, Peikun Liu, Ruixi Yu, Rongjie Bai, Yiyang Liu, Tonghui Ma, Qiang Cao, Qiang Lu

“…The OncoUrine test showed sensitivity of 77%, specificity of 78%, positive predictive value of 62%, and negative predictive value (NPV) of 88% for residual tumor, while exfoliated cytology showed corresponding values of 20%, 96%, 69%, and 72%. …”
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Postprandial triglyceride-rich lipoproteins as predictors of carotid atherosclerosis in individuals with normal fasting lipid profiles: a prospective follow-up study by Yilin Hou, Yilin Hou, Peipei Tian, Peipei Tian, Guangyao Song, Guangyao Song, Guangyao Song, An Song, Dandan Liu, Dandan Liu, Zhimin Wang, Zhimin Wang, Yihe Shi, Yihe Shi, Yale Tang, Yale Tang, Xiaoyu Wang, Xiaoyu Wang, Luxuan Li, Luxuan Li, Luping Ren, Luping Ren, Luping Ren

“…This study explores the oral fat tolerance test (OFTT) for its predictive value in CAS, using postprandial TRL levels as a diagnostic biomarker.MethodsA total of 107 volunteers with normal fasting lipid profiles and no CAS at baseline were included. …”
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Prediction of sinus rhythm maintenance after radiofrequency ablation in patients with atrial fibrillation using speckle tracking echocardiography and dynamics of left atrial struct... by T. I. Musin, Z. A. Bagmanova, D. A. Gareev, V. G. Rudenko, N. Sh. Zagidullin

“…The baseline LA S and SR values have a high predictive value for AF recurrence in patients after RFA. …”
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Assessment of the clinical efficacy of the heart spectrum blood pressure monitor for diagnosis of atrial fibrillation: An unblinded clinical trial. by Wei-Fong Kao, Sen-Kuang Hou, Chun-Yao Huang, Chun-Chieh Chao, Chung-Chih Cheng, Yi-Jung Chen

“…The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 97%, 97%, 97%, and 97%, respectively, for method 1; 90%, 100%, 100%, and 91%, respectively, for method 2; and 100%, 94%, 94%, and 100%, respectively, for method 3. …”
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Cardiac biomarkers for risk stratification in newly diagnosed high-risk multiple myeloma in the GMMG-CONCEPT trial by Lisa B. Leypoldt, Linlin Guo, Britta Besemer, Mathias Hänel, Marc-S. Raab, Christoph Mann, Christian S. Michel, Hans Christian Reinhardt, Igor Wolfgang Blau, Martin Görner, Yon-Dschun Ko, Maike de Wit, Hans Salwender, Christof Scheid, Ullrich Graeven, Rudolf Peceny, Peter Staib, Annette Dieing, Hartmut Goldschmidt, Carsten Bokemeyer, Tanja Zeller, Dirk Westermann, Katja C. Weisel, Raphael Twerenbold, Antonia Beitzen-Heineke

“…We investigated the predictive value of cardiac biomarkers for onset of CVAE in patients with newly diagnosed high-risk multiple myeloma treated with isatuximab, carfilzomib, lenalidomide, and dexamethasone in the GMMG-CONCEPT study (NCT03104842). …”
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Multi-cohort study in gastric cancer to develop CT-based radiomic models to predict pathological response to neoadjuvant immunotherapy by Ze-Ning Huang, Hao-Xiang Zhang, Yu-Qin Sun, Xing-Qi Zhang, Yi-Fen Lin, Cai-Ming Weng, Chao-Hui Zheng, Ping-Li, Jia-Bin Wang, Qi-Yue Chen, Long-Long Cao, Mi Lin, Ru-Hong Tu, Chang-Ming Huang, Jian-Xian Lin, Jian-Wei Xie

“…Specifically, within DC, the LightGBM model attained an AUC of 0.828, an overall accuracy of 0.791, a Cohen’s kappa coefficient of 0.552, a sensitivity of 0.742, a specificity of 0.818, a positive predictive value (PPV) of 0.586, a negative predictive value (NPV) of 0.867, a Matthews correlation coefficient (MCC) of 0.473, and a balanced accuracy of 0.780. …”
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A modified screening protocol for ARDS in patients with respiratory support based on SpO2 and FiO2: A single-center prospective, observational study by Yan Xia, Qiancheng Xu, Zhiyuan Guo, Huijuan Zhang, Yingya Cao, Yupeng Qi, Qun Chen, Weihua Lu

“…The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of the new diagnosis progress for ARDS were 91.1 %, 76.7 %, 89.6 %, 79.6 %, and 83.9 %, respectively. …”
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Comparing Claims Data to Stroke and Bleeding in the NCDR Left Atrial Appendage Occlusion Registry by Kamil F. Faridi, MD, MSc, James V. Freeman, MD, MPH, MS, Yongfei Wang, MS, Lucy Pereira, BA, Sarah Zimmerman, MS, Daniel J. Friedman, MD, Richa Sharma, MD, MPH, Angela Y. Higgins, MD, Bobak J. Mortazavi, PhD, Joseph S. Ross, MD, MHS, Harlan M. Krumholz, MD, SM, Robert W. Yeh, MD, MSc, MD, MPH, Jeptha P. Curtis, MD

“…Primary diagnosis International Classification of Diseases-Tenth Revision codes from inpatient hospitalizations were compared to adjudicated registry-reported stroke and major bleeding events after discharge, including estimation of sensitivity and positive predictive value of claims for identifying registry-reported events. …”
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Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predictin... by Anuradhani Kasturiratne, Hithanadura Janaka de Silva, Chamila Mettananda, Anuradha Supun Dassanayake, Norihiro Kato, Rajitha Wickremasinghe, Maheeka Solangaarachchige, Prasanna Haddela

“…In the external validation cohort, the sensitivity, specificity, positive predictive value, negative predictive value, and calibration of the 6-variable ML model and the WHO risk charts, respectively, were: 70.3%, 94.9%, 87.3%, 86.6%, χ2=8.22, p=0.41 and 23.7%, 79.0%, 35.8%, 67.7%, χ2=81.94, p<0.0001.Conclusions ML-based models derived from a cohort of Sri Lankans improved the overall accuracy of CV-risk prediction compared with the WHO risk charts for this cohort of Southeast Asians.…”
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One-step diagnosis of infection and lung cancer using metagenomic sequencing by Shaoqiang Li, Yangqing Zhan, Yan Wang, Weilong Li, Xidong Wang, Haoru Wang, Wenjun Sun, Xuefang Cao, Zhengtu Li, Feng Ye

“…In lung cancer diagnosis, Onco-mNGS showcases excellent diagnostic capabilities with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value reaching 88.46%, 100%, 91.41%, 100%, and 90.98%, respectively. …”
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The ratio of blood urea nitrogen to high-density lipoprotein cholesterol: a novel risk-stratifying tool for infective endocarditis by Qi Wang, Jing-wen Li, Shuai-zheng Che, Jie-leng Huang, Mei Jiang, Jun-quan Lu, Di Wu, Dan-qing Yu, Xue‑biao Wei

“…Conclusions BHR offers a high predictive value for short-term mortality in IE, positioning it as a potential stratification tool for critical care triage. …”
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Association between elevated serum REG Iα levels and eGFR decline in patients with chronic kidney disease: a cross-sectional study in eastern China by Ling Li, Hui Wang, Sheng Chen, Xi Huang, Bin Wang, Nan Huang, Rolf Graf, Xiaodong Wu, Xiangyun Zhu, Zhiyi Shu, Xiuxiu Hu, Jinfang Sun, Pingsheng Chen, Jianling Bai

“…In CKD screening, serum REG Iα demonstrated strong diagnostic performance, with an area under the receiver operating characteristic curves (AUC) of 0.860 (0.813–0.899), providing a sensitivity of 71.63%, a specificity of 86.89%, a positive predictive value of 94.30% and a negative predictive value of 46.85%. …”
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