A Pre-Protocol/Post-Protocol Quality Improvement Initiative Specific to One Hospital for Security of Pathologic Specimen

ObjectiveThe objective of this study was to reduce errors in a pathologic specimen with the help of a protocol systematizing the pathology specimen management process in the operating room.Materials and methodThis quasi-experimental study was carried out in the operating room unit of a research and training hospital. A protocol systematizing the process of specimen management in secure surgical pathology and prepared in light of the current literature was used as an intervention, and the effectiveness of the protocol was tested.ResultsIt was determined that the rate of adverse events decreased from .3226% (68 of 21,078) to .032% (6 of 18,706) after the protocol systematizing the surgical pathology specimen management process prepared by the researchers, and the protocol was found to be effective by 90% (P = .03).ConclusionBased on the data obtained in this study, we recommend the use of a pathologic specimen management protocol in the operating room.     

Preoperative comprehensive malignancy risk estimation for thyroid nodules: Development and verification of a network-based prediction model

BackgroundIn order to avoid excessive treatment of thyroid nodules in the clinic, it is necessary to find a simple and practical analysis method to comprehensively and accurately reflect benign or malignant thyroid nodules. This study aimed to construct and validate a comprehensive and reliable network-based predictive model using a variety of imaging and laboratory criteria for thyroid nodules to stratify the risk of malignancy prior to surgery.MethodsWe retrospectively analyzed data from patients who underwent surgical treatment for thyroid nodules at the Thyroid and Breast Diagnosis and Treatment Center of Weifang Hospital of Traditional Chinese Medicine between January 2018 and December 2020. Binary logical regression analysis was performed to predict whether nodules were malignant or benign. The developmental dataset included 457 patients (January 2018–December 2020). The validation set included separate data points (n = 225, January 2018–December 2020).ResultsIn this study, criteria that showed significant predictive value for malignant nodules included TI-RADS: 4b (p = 0.065); Bethesda IV, Bethesda V, Bethesda VI (P < 0.0001); BRAF^V600E mutation (P < 0.0001); Calcitonin>5 pg/ml (p = 0.0037); and FNA-Tg>30 ng/ml (p = 0.0003). A 10-grade risk scoring system was developed. The risk of malignancy risk ranged from 2.06% to 100% and was positively associated with increasing risk grade. The areas under the receiver-operating characteristic curve of the development and validation sets were 0.972 and 0.946, respectively.ConclusionA simple, comprehensive and reliable web-based predictive model was designed using a variety of imaging and laboratory criteria to stratify thyroid nodules by probability of malignancy.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

老年下肢骨折患者行手术室人性化护理的效果

目的评价手术室人性化护理干预对老年下肢骨折患者手术以及术后恢复情况的影响,以加速老年骨折患者康复。方法选择我院近年来收治手术治疗的老年下肢骨折患者,总计100例。结合手术室护理方法进行对照组和研究组随机分组,对照组老年下肢骨折患者采取常规护理,研究组老年下肢骨折患者采取手术室人性化护理干预。比较两组老年下肢骨折患者的手术情况,包括情绪评分、生活质量评分、术后并发症以及护理满意度。结果研究组老年下肢骨折患者SAS、SDS、SF-36评分均明显优于对照组,且安全性和护理满意度均明显高于对照组,观察指标数据差异有统计学意义,P<0.05。结论予以老年下肢骨折患者手术室人性化护理干预满足了患者的护理需求,进一步稳定了患者的情绪、身体,从而提高了患者手术安全性。     

目标教学在手术室护理带教中的应用效果观察

目的观察目标教学在手术室护理带教中的应用效果。方法选取2018年9月—2019年6月期间,医院手术室的48名实习护士,随机分为对照组和试验组,每组各24例。对照组行传统护理带教方法,试验组行目标教学带教。对比带教后两组考核成绩及带教满意度。结果带教后,试验组职业防护意识、风险意识、无菌观念、常见小中手术配合熟练度、常见小中手术护理要点掌握、基本工作职责及流程掌握等评分与对照组差异具有统计学意义(P<0.05)。试验组专科操作、操作测评及理论测评成绩与对照组差异具有统计学意义(P<0.05)。结论于手术室护理带教中应用目标教学,具有理想效果,有助于提升考核成绩及专业技能。     

工勤员主管与护士长协作对手术室管理质量的影响

目的探讨手术室工勤员主管和护士长协作管理模式在手术室工勤员管理中的应用效果。方法于2019年开始设置工勤员主管1名,协作护士长进行工勤员管理。评价实施前后管理效果。结果实施协作管理模式后,工勤员月质量考核分、手术室环境卫生检查得分、医护人员及工勤员满意度评分显著高于管理前(均P<0.01),工作缺陷减少。结论工勤员主管和护士长协作管理模式有利于提高手术室工勤员工作质量,提高工勤员和医护人员满意度。     

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

ObjectivesDespite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD).MethodsIn this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the “pROC” package of R (version 1.16.2).ResultsThe area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912–1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment.ConclusionsOur deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.     

Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography

BackgroundCompared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort.MethodsThree hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments.ResultsML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%–79.5%] vs. 54.8% [45.7%–63.8%]), LAD (79.3 [73.9–84.0] vs. 59.6 [53.5–65.6]), LCX (84.1 [76.0–90.3] vs. 63.7 [54.1–72.6]), proximal (81.5 [74.6–87.1] vs. 63.8 [55.9–71.2]), middle (81.2 [75.7–85.9] vs. 59.4 [53.0–65.6]) and distal stenosis location (67.4 [57.0–76.6] vs. 51.6 [41.1–62.0]).ConclusionIn a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.