Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarker.

The identification of new tumor biomarkers for patient stratification before therapy, for monitoring of disease progression, and for characterization of tumor biology plays a crucial role in cancer research. The status of these biomarkers is mostly scored manually by a pathologist and such scores typically, do not consider the spatial heterogeneity of the protein's expression in the tissue. Using advanced image analysis methods, marker expression can be determined quantitatively with high accuracy and reproducibility on a per-cell level. To aggregate such per-cell marker expressions on a patient level, the expression values for single cells are usually averaged for the whole tissue. However, averaging neglects the spatial heterogeneity of the marker expression in the tissue. We present two novel approaches for quantitative scoring of spatial marker expression heterogeneity. The first approach is based on a co-occurrence analysis of the marker expression in neighboring cells. The second approach accounts for the local variability of the protein's expression by tiling the tissue with a regular grid and assigning local spatial heterogeneity phenotypes per tile. We apply our novel scores to quantify the spatial expression of four different membrane markers, i.e., HER2, CMET, CD44, and EGFR in immunohistochemically (IHC) stained tissue sections of colorectal cancer patients. We evaluate the prognostic relevance of our spatial scores in this cohort and show that the spatial heterogeneity scores clearly outperform the marker expression average as a prognostic factor (CMET: p-value=0.01 vs. p-value=0.3).

Quality of life, infection control, and complication rates using a novel custom-made articulating hip spacer during two-stage revision for periprosthetic joint infection.

INTRODUCTION: Two-stage revision remains the gold standard treatment for most chronically infected and complex total hip arthroplasty infections. To improve patient outcome and reduce complication rates, we have developed a novel custom-made articulating hip spacer technique and present our short-term results. MATERIALS AND METHODS: Between November 2017 and November 2019, 27 patients (mean age 70 years) underwent two-stage revision for periprosthetic joint infection of the hip using the articulating spacer design described here. We retrospectively analyzed spacer-related complications as well as rates for complication, infection control, and implant survivorship after final reimplantation. Furthermore, we prospectively collected patient-reported health-related quality of life (HRQoL) scores prior to spacer implantation, with the spacer and after reimplantation of the new prosthesis. RESULTS: An additional round of spacer exchange was performed in two patients (8.3%), persistent wound discharge was the reason in both cases. We had one (4.2%) spacer-related mechanical complication, a dislocation that was treated with closed reduction. After reimplantation, infection control was achieved in 96% with an implant survivorship of 92% after a mean follow-up time of 19 (range 7-32, SD 7.2) months. While the scores for VR-12 MCS, VAS hip pain and patient-reported overall satisfaction significantly improved after first stage surgery, the scores for WOMAC, UCLA and VR-12 PCS significantly improved after second stage surgery. CONCLUSIONS: Our two-stage approach for periprosthetic joint infection shows high infection eradication and implant survivorship rates at short-term follow-up. Spacer-related complication rates were low, and we achieved high patient satisfaction rates and low pain levels already during the spacer period. To further simplify comparison between different spacer designs, we propose a new hip spacer classification system.

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR = 0.539, p = 0.004 and second system: HR = 0.525, p = 0.003) compared to TC scoring by pathologists (HR = 0.574, p = 0.01).

Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning.

The clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine-learning-based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1×10-5). Critical to improving MIBC survival rates, our method correctly classifies 71.4% of the patients who succumb to MIBC, which is significantly more than the 28.6% of the current clinical gold standard, the TNM staging system.

[Changes of sex ratio and prevalence in transgender teenagers over the past 15 years]. / Transidentität in Jugend und Adoleszenz: Zur Veränderung der Sexratio und der Prävalenz in den letzten eineinhalb Jahrzehnten.

Changes of sex ratio and prevalence in transgender teenagers over the past 15 years Abstract. Evaluation of authors' 1434 expert opinions from 2005-2019 on transgender applicants (420 younger than 20 years old) for legal change of name and gender according to German "Law on Transsexuality" showed (1) in teenage applicants substantial changes of sex ratio from 2:1 to 10:1 in favour of transmales; (2) while prevalence of teenage transfemales during this period remained unchanged, prevalence of transmales rose significantly. According to our data, transgender teenagers are nowadays primarily natal females. Clinical and sociocultural aspects of these changes are discussed.

Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer-specific death from digital images of immunohistochemically (IHC) stained tissue microarrays (TMAs). Specifically, we evaluated a cohort of 248 gastric cancer patients using convolutional neural networks (CNNs) in an end-to-end weakly supervised scheme independent of subjective pathologist input. To account for the time-to-event characteristic of the outcome data, we developed new survival models to guide the network training. In addition to the standard H&E staining, we investigated the prognostic value of a panel of immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki67). Our CNN-derived risk scores provided additional prognostic value when compared to the gold standard prognostic tool TNM stage. The CNN-derived risk scores were also shown to be superior when systematically compared to cell density measurements or a CNN score derived from binary 5-year survival classification, which ignores time-to-event. To better understand the underlying biological mechanisms, we qualitatively investigated risk heat maps for each marker which visualised the network output. We identified patterns of biological interest that were related to low risk of cancer-specific death such as the presence of B-cell predominated clusters and Ki67 positive sub-regions and showed that the corresponding risk scores had prognostic value in multivariate Cox regression analyses (Ki67&CD20 risks: hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.15-1.89, p = 0.002; CD20&CD68 risks: HR = 1.33, 95% CI = 1.07-1.67, p = 0.009). Our study demonstrates the potential additional value that deep learning in combination with a panel of IHC markers can bring to the field of precision oncology.

Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma.

In the context of precision medicine with immunotherapies there is an increasing need for companion diagnostic tests to identify potential therapy responders and avoid treatment coming along with severe adverse events for non-responders. Here, we present a retrospective case study to discover image-based signatures for developing a potential companion diagnostic test for ipilimumab (IPI) in malignant melanoma. Signature discovery is based on digital pathology and fully automatic quantitative image analysis using virtual multiplexing as well as machine learning and deep learning on whole-slide images. We systematically correlated the patient outcome data with potentially relevant local image features using a Tissue Phenomics approach with a sound cross validation procedure for reliable performance evaluation. Besides uni-variate models we also studied combinations of signatures in several multi-variate models. The most robust and best performing model was a decision tree model based on relative densities of CD8+ tumor infiltrating lymphocytes in the intra-tumoral infiltration region. Our results are well in agreement with observations described in previously published studies regarding the predictive value of the immune contexture, and thus, provide predictive potential for future development of a companion diagnostic test.

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive bladder cancer patients. Furthermore, tumour budding was found to be correlated to TNM (p = 0.00089) and pT (p = 0.0078) staging. A novel classification and regression tree model was constructed to stratify all stage II, III, and IV patients into three new staging criteria based on disease specific survival. For the stratification of non-metastatic patients into high or low risk of disease specific death, our decision tree model reported that tumour budding was the most significant feature (HR = 2.59, p = 0.0091), and no clinical feature was utilised to categorise these patients. Our findings demonstrate that tumour budding, quantified using automated image analysis provides prognostic value for muscle invasive bladder cancer patients and a better model fit than TNM staging.

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.

The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

Repetitive use of levosimendan in advanced heart failure: need for stronger evidence in a field in dire need of a useful therapy.

Patients in the latest stages of heart failure are severely compromised, with poor quality of life and frequent hospitalizations. Heart transplantation and left ventricular assist device implantation are viable options only for a minority, and intermittent or continuous infusions of positive inotropes may be needed as a bridge therapy or as a symptomatic approach. In these settings, levosimendan has potential advantages over conventional inotropes (catecholamines and phosphodiesterase inhibitors), such as sustained effects after initial infusion, synergy with beta-blockers, and no increase in oxygen consumption. Levosimendan has been suggested as a treatment that reduces re-hospitalization and improves quality of life. However, previous clinical studies of intermittent infusions of levosimendan were not powered to show statistical significance on key outcome parameters. A panel of 45 expert clinicians from 12 European countries met in Rome on November 24-25, 2016 to review the literature and envision an appropriately designed clinical trial addressing these needs. In the earlier FIGHT trial (daily subcutaneous injection of liraglutide in heart failure patients with reduced ejection fraction) a composite Global Rank Score was used as primary end-point where death, re-hospitalization, and change in N-terminal-prohormone-brain natriuretic peptide level were considered in a hierarchical order. In the present study, we tested the same end-point post hoc in the PERSIST and LEVOREP trials on oral and repeated i.v. levosimendan, respectively, and demonstrated superiority of levosimendan treatment vs placebo. The use of the same composite end-point in a properly powered study on repetitive levosimendan in advanced heart failure is strongly advocated.

The effect of a checklist on the quality of post-anaesthesia patient handover: a randomized controlled trial.

OBJECTIVE: Patient handover is an important element of continuity, quality and safety in patient care. Handover without standardized protocols is prone to information loss and might be a possible danger to patient safety. Checklists are established methods that help to structure complex processes in other high-risk fields such as aviation. In the past few years, their implementation has attracted research interest in medicine. We hypothesize that a checklist for handover between anaesthesiologist and post-anaesthesia care unit nurse will increase the amount of information transfer during patient handover after anaesthesia. DESIGN AND SETTING: A total of 120 post-anaesthesia patient handovers were recorded on video and analyzed. Forty handovers before the implementation of the checklist and 80 after the implementation of the checklist, randomized into two groups: with and without the use of the checklist. MAIN OUTCOME MEASURES: An overall number of items handed over, handover of specific items and duration of the handover were analyzed. RESULTS: With the use of the written checklist, the overall items handed over increased significantly from a median of 32.4-48.7%. The duration of handover increased from a median of 86-121 s. Instructions about items that should be included in handovers, but without the use of a written checklist, was not associated with an increase in the number of items handed over or duration of the interview. CONCLUSIONS: This study suggests that the use of a checklist for post-anaesthesia handover might improve the quality of patient handover by increasing the information handed over.

Electroencephalography during out-of-hospital cardiopulmonary resuscitation.

BACKGROUND: At the present time there is no parameter that can estimate the quality of cerebral perfusion and possible success of cerebral resuscitation during advanced cardiac life support (ACLS) efforts. In recent years, various attempts have been made to use electroencephalography (EEG)-based cerebral neuromonitoring to assess the effectiveness of cardiopulmonary resuscitation (CPR). OBJECTIVES: The Cerebral State Monitor M3 (Danmeter A/S, Odense, Denmark) is a portable, single-channel EEG monitor that provides the user with different EEG-based parameters and the raw waveform EEG to measure cerebral activity. CASE REPORT: We report two cases of out-of-hospital CPR with single-channel EEG monitoring conducted parallel to ACLS with external chest compressions. We demonstrate an artifact in waveform EEG recordings that is caused by the external chest compressions, and that leads to a miscalculation of the Burst Suppression Ratio and Cerebral State Index. CONCLUSION: These cases suggest that digitally processed EEG-monitoring is not a useful tool during CPR.

Single-channel electroencephalography of epileptic seizures in the out-of-hospital setting: an observational study.

OBJECTIVES: To evaluate whether single-channel electroencephalography (EEG) recording can be conducted in the out-of-hospital setting and whether it can be used to record electrographic signs of convulsive epileptic seizures. METHODS: This prospective observational feasibility study included patients who presented with a recent or ongoing epileptic seizure during out-of-hospital emergency treatment. Bifrontal single-channel EEG recordings were conducted by ambulance physicians throughout the initial treatment. The data recorded were analysed for the quality of recording and the occurrence of ictal EEG patterns. RESULTS: There were 45 adult patients who had a recent or an ongoing epileptic seizure in the study group and 15 patients with no neurological disorders in the control group. The median percentage of time during which no artefacts were detected by the device was 88.0% in the study group and 96.0% in the control group. EEG recordings for 3 out of 45 (6.6%) patients were of poor quality and not evaluable. Spike/wave or polyspike patterns were found in 98% and 100% of patients in the study and control groups, respectively, whereas the occurrence of periodic epileptiform discharges and delta waves with spikes showed a sensitivity and specificity of 100% (10/10) for the presence of an ongoing epileptic seizure. CONCLUSIONS: Single-channel EEG can be performed outside the hospital and yields useful recordings in most patients with acceptable rates of artefact. The diagnosis of generalised convulsive epileptic seizures by offline analysis of out-of-hospital EEG showed a high sensitivity and specificity when compared with the clinical diagnosis.

Single-channel amplitude integrated EEG recording for the identification of epileptic seizures by nonexpert physicians in the adult acute care setting.

OBJECTIVE: Although several studies have shown the potential of amplitude integrated electroencephalography (aEEG) in detecting neonatal seizures, no publications have evaluated the diagnostic use of aEEG for the detection of seizures in adult patients. METHODS: In this prospective blinded observational study, bifrontal single-channel electroencephalography (EEG) recordings were performed with a portable EEG monitor (CSM M3 ICU, Danmeter-Goalwick Holdings Limited, Odense, Denmark) during the out-of-hospital care of emergency cases. Four intensive care unit (ICU) physicians received training in the interpretation of aEEG recordings. After the training they evaluated the stored aEEG traces for the presence of epileptic seizure activity during the recording time. The physicians were blinded to the clinical data of the patients. The results obtained were compared with the clinical diagnosis and the evaluation of the raw EEG signal. The level of interrater agreement was quantified using Fleiss' ĸ. RESULTS: The aEEG traces from 10 patients with generalized epileptic seizures and 46 patients without seizures were analysed. Overall, the nonexpert ICU physicians failed to identify recordings obtained from patients with seizures reliably, when compared with clinical diagnosis and the single-channel EEG results (mean sensitivity 40%, range 40-60%; mean specificity 89%, range 87-93%). Agreement between observers was high for the cases with seizures ( ĸ = 0.80 ± 0.13). Patients who suffered status epilepticus during the recordings were difficult to identify by most raters. CONCLUSION: Recording of aEEG without access to the raw EEG data is not a reliable diagnostic tool for the identification of epileptic seizures in the hands of nonexpert ICU physicians.

Cognition Network Technology prototype of a CAD system for mammography to assist radiologists by finding similar cases in a reference database.

PURPOSE: We present a new approach for computer-aided detection and diagnosis in mammography based on Cognition Network Technology (CNT). Originally designed for image processing, CNT has been extended to also perform context- and knowledge-driven analysis of tabular data. For the first time using this technology, an application was created and evaluated for fully automatic searching of patient cases from a reference database of verified findings. The application aims to support radiologists in providing cases of similarity and relevance to a given query case. It adopts an extensible and knowledge-driven concept as a similarity measure. METHODS: As a preprocessing step, all input images from more than 400 patients were fully automatically segmented and the resulting objects classified--this includes the complete breast shape, the position of the mammilla, the pectoral muscle, and various potential candidate objects for suspicious mass lesions. For the similarity search, collections of object properties and metadata from many patients were combined into a single table analysis project. Extended CNT allows for a convenient implementation of knowledge-based structures, for example, by meaningfully linking detected objects in different breast views that might represent identical lesions. Objects from alternative segmentation methods are also be considered, so as to collectively become a sufficient set of base-objects for identifying suspicious mass lesions. RESULTS: For 80% of 112 patient cases with suspicious lesions, the system correctly identified at least one corresponding mass lesion as an object of interest. In this database, consisting of 1,024 images from a total of 303 patients, an average of 0.66 false-positive objects per image were detected. An additional testing database contained 480 images from 120 patients, 15 of whom were annotated with suspicious mass lesions. Here, 47% (7 out of 15) of these were detected automatically with 1.13 false-positive objects per image. A diagnosis is predicted for each patient case by applying a majority vote from the reference findings of the ten most similar cases. Two separate evaluation scenarios suggest a fraction of correct predictions of respectively 79 and 76%. CONCLUSION: Cognition Network Technology was extended to process table data, making it possible to access and relate records from different images and non-image sources, such as demographic patient data or parameters from clinical examinations. A prototypal application enables efficient searching of a patient and image database for similar patient cases. Using concepts of knowledge-driven configuration and flexible extension, the application illustrates a path to a new generation of future CAD systems.

Comparison of the over-the-head, lateral and alternating positions during cardiopulmonary resuscitation performed by a single rescuer with a bag--valve--mask device.

BACKGROUND: The 2005 guidelines for cardiopulmonary resuscitation (CPR) do not include a statement on performance of basic life support by a single healthcare professional using a bag-valve-mask device. Three positions are possible: chest compressions and ventilations from over the head of the casualty (over-the-head CPR), from the side of the casualty (lateral CPR), and chest compressions from the side and ventilations from over the head of the casualty (alternating CPR). The aim of this study was to compare CPR quality of these three positions. METHODS: 102 healthcare professionals were randomised to a crossover design and performed a 2-min CPR test on a manikin for each position. RESULTS: The hands-off time over a 2-min interval was not significantly different between over-the-head (median 31 s) and lateral (31 s) CPR, but these compared favourably with alternating CPR (36 s). Over-the-head CPR resulted in significantly more chest compressions (155) compared with lateral (152) and alternating CPR (149); the number of correct chest compressions did not differ significantly (119 vs 122 vs 109). Alternating CPR resulted in significantly less inflations (eight) compared with over-the-head (ten) and lateral CPR (ten). Lateral CPR led to significantly less correct inflations (three) compared with over-the-head (five) and alternating CPR (four). CONCLUSIONS: In the case of a single healthcare professional using a bag-valve-mask device, the quality of over-the-head CPR is at least equivalent to lateral, and superior to alternating CPR. Because of the potential difficulties in bag-valve-mask ventilation in the lateral position, the authors recommend over-the-head CPR.

Histopathological, radiological and clinical aspects of the temporal assignment of scaphoid non-union.

OBJECTIVE: The aim of this study was to evaluate the correlation between clinical, radiological and histopathological signs of scaphoid non-unions (SNU) with regard to the age of the fracture, primarily because this is relevant for therapy and compensation claims. MATERIALS AND METHODS: Thirty-eight patients with SNU underwent clinical and radiological examination of the wrist prior to surgery. Preoperative X-rays of the wrist were analysed using the scores of Herbert and Fisher, Filan and Herbert, Trojan and Jahna, Gupta as well as scaphoid non-union advanced collapse. Sclerotic bone of the SNU was resected during surgery. Resected material was evaluated histologically after staining with hematoxylin-eosin and periodic acid-Schiff reaction. Radiological and histological examinations were performed by independent investigators in a blinded fashion. RESULTS: The preoperative range of motion of the injured compared to the contralateral wrist was significantly reduced for the dorsi- and palmar flexion and for the radial and ulnar deviation; however, this reduction was not time dependent. There was no significant correlation between the radiological results and the age of the SNU, while fibrous tissue or fibrocartilage in the fracture gap was present in all cases. Significantly less fibrous or fibrocartilage bone cysts (p = 0.041) and bone remodelling (p = 0.031) were seen in older SNU (at 45 months). Definitive sclerotic bone covering of the fracture edges was significantly more common in older SNU (p = 0.035). CONCLUSION: Radiological and most of the conventional histological findings do not correlate with time after initial injury.

A comparison between over-the-head and standard cardiopulmonary resuscitation performed by two rescuers: a simulation study.

BACKGROUND: In cardiopulmonary resuscitation (CPR) of a patient with an unsecured airway performed by two health care professionals, two methods are possible: 1) Standard CPR according to the guidelines, with one rescuer performing chest compressions from the side and the other rescuer performing ventilations from over the head of the patient. Additional tasks (like attaching the electrocardiogram and defibrillator) must be performed by the second rescuer during the time between ventilations. 2) Over-the-head CPR, with one rescuer performing chest compressions and ventilations from over the head and the other rescuer performing additional tasks. OBJECTIVES: The aim of this study was to compare the quality of CPR achieved by the two methods. METHODS: After a standardized theoretical introduction and practical training, 106 medical students with limited knowledge and training in CPR participated in this randomized crossover study. Students performed a 2-min CPR test of standard CPR in both positions and over-the-head CPR alone on a manikin. RESULTS: Standard CPR led to a significantly shorter hands-off-time over a 2-min interval than over-the-head CPR (median 25 s [interquartile range (IQR) 22-26 s] vs. 38 s [IQR 36-43 s], respectively, p < 0.001), and significantly more chest compressions (167 [IQR 158-176] vs. 142 [IQR 132-150], respectively, p < 0.001), more correct chest compressions (72 [IQR 11-136] vs. 45 [IQR 13-88], respectively, p = 0.004), inflations (10 [IQR 10-10] vs. 8 [IQR 8-8], respectively, p < 0.001), and correct inflations (5 [IQR 2-7] vs. 3 [IQR 1-4], respectively, p < 0.001). CONCLUSIONS: In the case of a two-professional-rescuer CPR scenario, standard CPR enables a quantitatively better resuscitation than over-the-head CPR.