白内障术后生物学参数变化及IOL计算公式的准确性研究

目的：运用新型光学生物测量仪IOL Master 700测量白内障超声乳化手术前后眼部生物学参数的变化，并探讨人工晶状体(IOL)屈光度数计算公式的选择。

方法：前瞻性研究。收集2021-01/06在苏州大学附属第一医院就诊的白内障患者52例57眼。术前和术后3mo使用IOL Master 700完成眼轴长度(AL)、前房深度(ACD)、角膜曲率(Km)的测量并分析。对不同IOL公式计算时预留的目标屈光值与术后3mo全自动验光仪实际屈光值结果进行比较并分析。

结果：手术前后测量的AL平均值分别为24.20±1.86、24.09±1.86mm，术后AL缩短了0.11mm； ACD值分别为3.08±0.44、4.55±0.36mm(P<0.001)，术后ACD加深1.49mm； Km值分别为44.14±1.86、44.14±1.82D(P>0.05)。术前选用Barrett Universal Ⅱ公式所测结果的屈光误差最小，其次是Holladay Ⅱ及SRK/T公式，Holladay Ⅰ公式所测结果的误差最大(P<0.05)。

结论：白内障术后AL缩短以及ACD加深，度数测算时可考虑增加0.1mm的校正因子。IOL屈光度数计算公式中Barrett Universal Ⅱ公式预测性最佳，其次是Holladay Ⅱ及SRK/T公式。     

The personal limitations checklist: human factors insights from air accidents to reduce intraoperative harm

High Reliability Organisations (HRO), including healthcare and aviation, have a common focus on risk management. The human element is a ‘weak link’ which may result in accidents or adverse events taking place. Surgeons and other healthcare professionals can learn from aviation's rigorous approach to the role of human factors (HF) in such events, and how we can minimise them. Air Accident Investigation Branch (AAIB) reports show that fatal accidents are frequently caused by pilots flying outside their own personal limits, those of the aircraft or environment. Similarly, patient morbidity or mortality may occur if surgeons work outside personal their capability, with poor procedure selection and patient optimisation, or with a team or theatre environment not suited to the procedure. We introduce the personal limitations checklist – a tool adapted from aviation that allows surgeons to define their limits in advance of any decision to operate, and develop critical self-reflection. It also allows management of patient expectations, shared decision making, and flattening of team hierarchy. The minimum skills, patient characteristics, team and theatre resources for any given procedure to proceed are defined. If the surgeon is ‘out of limits’, redressing these factors, seeking additional assistance, or thorough patient consenting may be required for the safe conduct of the procedure. We explore external pressures that could cause a surgeon to exceed both personal and organisational limits.     

应用 σ度量对深圳市龙华区三家综合医院临床生化检验结果互认的评价研究

目的　探讨 σ度量在深圳市龙华区三家综合医院临床生化检验结果互认中的应用价值。方法　收集三家医院 2019年广东省临检中心常规化学，特殊蛋白前两次的室间质评（ EQA）样本，经混匀并分割成比对样本送至各评价实验室进行检测；以卫生部临检中心（ NCCL)标准和生物学变异导出的允许总误差（ TEa%）为质量规范，通过加权法计算出各项目 2019年 1～ 6月期间室内质控在控数据的不精密度，以 2018年参加卫生部室间质评（ EQA）数据作为偏倚（ bias%）来源，计算三家医院 29个检验项目的 σ度量值并进行性能评价，利用质量目标指数（ QGI)提出改善方法；将评价项目的 σ度量值与直接比对试验进行比较，通过统计分析确定 σ截断值（ cut-off）。结果　①三家医院 29个生化项目的性能并不一致，将项目中、高值的 σ值进行配对 t检验，其 t值分别为 2.28，3.01和 0.74，按 α =0.05水准判断得出医院 A与 B，医院 A与 C间的性能差异有统计学意义，医院 B与 C间的性能差异无统计学意义，医院 A项目性能要优于 B，C医院。评估的 87个项目中有 53项 σ平均值 <6，根据 QGI判断，3个项目需优先改善准确度，10个项目需同步改善准确度和精密度，另 40个项目需优先改善精密度。②对医院间项目比对通过率进行配对 χ2检验，其 χ2分别为 5.33，6.25和 2.5，按α=0.05水准判断， B与 A医院比对通过率低于 C与 A医院、 C与 B医院，差异有统计学意义； C与 A医院比对通过率跟 C与 B医院差异无统计学意义。③将 σ度量值与直接比对试验进行比较，经配对 χ2检验，当 σ cut-off值取≥ 2σ， ≥ 3σ，≥ 4σ，≥ 5σ和≥ 6σ时，其 χ2分别为 7.20，0.00，9.09，15.06和 16.06，按 χ20.05，1=3.84，α=0.05水准判断，当σcut-off值取≥ 3σ时，两种比对方法的差异无统计学意义。结论　σ度量值可以单独用于评价医院间生化检验结果的互认，在判断项目是否具有可比性时，可考虑将 σ cut-off值设为≥ 3σ。     

Medical error disclosure in the Italian healthcare context: A delicate balance between ethical obligations and the principle of non-self-incrimination

Surprisingly, Italian legal doctrine and jurisprudence never systematically address the medical error disclosure issue. The topic of medical error communication represents a non-negligible profile of interest, relating to the possible detrimental effects on doctors who accuse themselves of conduct that does not comply with the lex artis. The effects of error disclosure on the effectiveness of insurance guarantees in civil and administrative liability are particularly relevant, as are the implications for ethical liability. Although the burden of reporting an error falls within the wide range of informative duties doctors hold, it still seems far from having found a factual statement in clinical practice, especially in Italy. This applies whether the error has a marginal or significant impact on the patient’s health. The reasons lay in a very contradictory legal framework. Doctors tend with increasing ease not to comply with their information obligation – especially in cases where fulfilling this duty means admitting a personal, professional error – to preserve the integrity of their professional images. This article aims to offer a brief overview of that topic in the context of Italian healthcare.     

Measurement errors in control risk regression: A comparison of correction techniques

Control risk regression is a diffuse approach for meta-analysis about the effectiveness of a treatment, relating the measure of risk with which the outcome occurs in the treated group to that in the control group. The severity of illness is a source of between-study heterogeneity that can be difficult to measure. It can be approximated by the rate of events in the control group. Since the estimate is a surrogate for the underlying risk, it is prone to measurement error. Correction methods are necessary to provide reliable inference. This article illustrates the extent of measurement error effects under different scenarios, including departures from the classical normality assumption for the control risk distribution. The performance of different measurement error corrections is examined. Attention will be paid to likelihood-based structural methods assuming a distribution for the control risk measure and to functional methods avoiding the assumption, namely, a simulation-based method and two score function methods. Advantages and limits of the approaches are evaluated through simulation. In case of large heterogeneity, structural approaches are preferable to score methods, while score methods perform better for small heterogeneity and small sample size. The simulation-based approach has a satisfactory behavior whichever the examined scenario, with no convergence issues. The methods are applied to a meta-analysis about the association between diabetes and risk of Parkinson disease. The study intends to make researchers aware of the measurement error problem occurring in control risk regression and lead them to the use of appropriate correction techniques to prevent fallacious conclusions.     

Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach

PurposeTo show that a deep learning (DL)–based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the “ground truth segmentation” than a conventional thresholding-based model.Materials and MethodsThis post hoc analysis included 36 patients with a diagnosis of hepatocellular carcinoma or other solid liver tumors who underwent conventional transarterial chemoembolization with an intraprocedural cone-beam CT. Semiautomatic segmentation of Lipiodol was obtained. Subsequently, a convolutional U-net model was used to output a binary mask that predicted Lipiodol deposition. A threshold value of signal intensity on cone-beam CT was used to obtain a Lipiodol mask for comparison. The dice similarity coefficient (DSC), mean squared error (MSE), center of mass (CM), and fractional volume ratios for both masks were obtained by comparing them to the ground truth (radiologist-segmented Lipiodol deposits) to obtain accuracy metrics for the 2 masks. These results were used to compare the model versus the threshold technique.ResultsFor all metrics, the U-net outperformed the threshold technique: DSC (0.65 ± 0.17 vs 0.45 ± 0.22, P < .001) and MSE (125.53 ± 107.36 vs 185.98 ± 93.82, P = .005). The difference between the CM predicted and the actual CM was 15.31 mm ± 14.63 versus 31.34 mm ± 30.24 (P < .001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22 ± 0.84 versus 2.58 ± 3.52 (P = .048) for the current model’s prediction and threshold technique, respectively.ConclusionsThis study showed that a DL framework could detect Lipiodol in cone-beam CT imaging and was capable of outperforming the conventionally used thresholding technique over several metrics. Further optimization will allow for more accurate, quantitative predictions of Lipiodol depositions intraprocedurally.     

Multiple Administrations of Intravenous Thrombolytic Therapy to a Stroke Mimic

BackgroundPatients who present emergently with focal neurological deficits concerning for acute ischemic stroke can be extremely challenging to diagnose and treat. Unnecessary administration of thrombolytics to potential stroke patients whose symptoms are not caused by an acute ischemic stroke—stroke mimics—may result in patient harm, although the overall risk of hemorrhagic complications among stroke mimics is low.Case ReportWe present a case of a stroke mimic patient with underlying psychiatric disease who was treated with intravenous alteplase on four separate occasions in four different emergency departments in the same city. Although he did not suffer hemorrhagic complications, this case highlights the importance of rapid exchange of health information across institutions to improve diagnostic quality and safety.Why Should an Emergency Physician Be Aware of This?Increased awareness of stroke mimics by emergency physicians may improve diagnostic safety for a subset of high-risk patients. Establishing rapid cross-institutional communication pathways that are integrated into provider's workflows to convey essential patient health information has potential to improve stroke diagnostic decision-making and thus represents an important topic for health systems research in emergency medicine.