Potassium additive algorithm for use in continuous renal replacement therapy

The performance of continuous renal replacement therapy (CRRT) brings about alterations in the serum potassium levels in patient. Potassium is an electrolyte essential for the regulation of nerve conduction and muscle contraction, particularly important in the case of cardiac muscle. This article describes the physiological mechanisms that affect potassium distribution throughout the body and also describes the effects of hypokalaemia or hyperkalaemia on the heart. This article justifies the need for serum potassium control during CRRT, which recognizes and reconciles the differing areas of responsibility of the medical and nursing staff. This article critically reviews the steps taken to develop and implement into clinical practice and evaluate a potassium additive algorithm. It also discusses the implications of this initiative.     

Can a flowchart improve the quality of bystander cardiopulmonary resuscitation?

Background

Since the introduction of basic life support in the 1950s, on-going efforts have been made to improve the quality of bystander cardiopulmonary resuscitation (CPR). Even though bystander-CPR can increase the chance of survival almost fourfold, the rates of bystander initiated CPR have remained low and rarely exceed 20%. Lack of confidence and fear of committing mistakes are reasons why helpers refrain from initiating CPR. The authors tested the hypothesis that quality and confidence of bystander-CPR can be increased by supplying lay helpers with a basic life support flowchart when commencing CPR, in a simulated resuscitation model.

Materials and methods

After giving written informed consent, 83 medically untrained laypersons were randomised to perform basic life support for 300s with or without a supportive flowchart. The primary outcome parameter was hands-off time (HOT). Furthermore, the participants’ confidence in their actions on a 10-point Likert-like scale and time-to-chest compressions were assessed.

Results

Overall HOT was 147 ± 30 s (flowchart) vs. 169 ± 55 s (non-flowchart), p = 0.024. Time to chest compressions was significantly longer in the flowchart group (60 ± 24 s vs. 23 ± 18 s, p < 0.0001). Participants in the flowchart group were significantly more confident when performing BLS than the non-flowchart counterparts (7 ± 2 vs. 5 ± 2, p = 0.0009).

Conclusions

A chart provided at the beginning of resuscitation attempts improves quality of CPR significantly by decreasing HOT and increasing the participants’ confidence when performing CPR. As reducing HOT is associated with improved outcome and positively impacting the helpers’ confidence is one of the main obstacles to initiate CPR for lay helpers, charts could be utilised as simple measure to improve outcome in cardiopulmonary arrest.     

On the reducibility of compartmental matrices

Compartmental analysis is the most important modeling tool in biology and medicine. The perturbation due to a tracer in a biomedical system usually leads to a linear system that can be described by a compartmental matrix. The reducibility of the compartmental matrix has important implications on the system, both analytically and computationally. This note provides an efficient algorithm to determine the reducibility of a compartmental system. Furthermore, if reducible, the algorithm provides a means to delineate the sub-systems to be successively solved.     

Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation

OBJECTIVES: In this pilot study, we intended to assess whether a procedure based on blind source separation (BSS) and subsequent partial reconstruction of magnetoencephalogram (MEG) recordings might enhance the differences between MEGs from Alzheimer's disease (AD) patients and elderly control subjects. MATERIALS AND METHODS: We analysed MEG background activity recordings acquired with a 148-channel whole-head magnetometer from 21 AD patients and 21 control subjects. Artefact-free epochs of 20 s were blindly decomposed using the algorithm for multiple unknown signals extraction (AMUSE), which arranges the extracted components by decreasing linear predictability. Thus, the components of diverse epochs and subjects could be easily compared. Every component was characterised with its median frequency and spectral entropy (denoted by fmedian and SpecEn, respectively). The differences between subject groups in these variables were statistically evaluated to find out which components could improve the subject classification. Then, these significant components were used to partially reconstruct the MEG recordings. RESULTS: The statistical analysis showed that the AMUSE components which provided the largest differences between demented patients and control subjects were ordered together. Considering this analysis, we defined two subsets, denoted by BSS-{15,35} and BSS-{20,30}, which included 21 components (15-35) and 11 components (20-30), respectively. We partially reconstructed the MEGs with these subsets. Then, the classification performance was computed with a leave-one-out cross-validation procedure for the case where no BSS was applied and for the partial reconstructions BSS-{15,35} and BSS-{20,30}. The BSS and component selection procedure improved the classification accuracy from 69.05% to 83.33% using f(median) with BSS-{15,35} and from 61.91% to 73.81% using SpecEn with BSS-{20,30}. CONCLUSION: These preliminary results lead us to think that the proposed procedure based on BSS and selection of significant components may improve the classification of AD patients using straightforward features from MEG recordings.     

Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier

Objective: Lung cancer is one of the unsafe diseases for human which reduces the patient life time. Generally, most of the lung cancers are identified after it has been spread into the lung parts and moreover it is difficult to find the lung cancer at the early stage. It requires radiologist and special doctors to find the tumoral tissue of the lung cancer. For this reason, the recommended work helps to segment the tumoral tissue of CT lung image in an effective way. Methods:  The research work uses hybrid segmentation technique to separate the lung cancer cells to diagnose the lung tumour. It is a technique which combines active contour along with Fuzzy c means to diagnose the tumoral tissue. Further the segmented portion was trained by Convolutional Neural Network (CNN) in order to classify the segmented region as normal or abnormal. Results: The evaluation of the proposed method was done by analyzing the results of test image with the ground truth image. Finally, the results of the implemented technique provided good accuracy, Peak signal to noise ratio (PSNR), Mean Square Error (MSE) value. In future the other techniques can be utilized to improve the details before segmentation. The proposed work provides 96.67 % accuracy. Conclusion: Hybrid segmentation technique involves several steps like preprocessing, binarization, thresholding, segmentation and feature extraction using GLCM.     

Regla «1-2-3» para la reconstrucción de defectos de mediano tamaño del cuero cabelludo

Background and objectiveExcision of cutaneous scalp tumors results in surgical defects that are difficult to repair because of poor distensibility in this area of the body. The main aim of this study was to develop a structured algorithm to help choose the best technique for reconstructing scalp defects.Material and methodsRetrospective study of patients who required surgical reconstruction following excision of a cutaneous scalp tumor. We excluded patients with defects that could be closed by simple direct suture and defects for which it was initially decided to use a skin graft or healing by secondary intention. The defects were classified into 5 groups according to the minimum distance between edges. The different reconstruction techniques used were evaluated in each group. The outcomes analyzed were complete defect closure, intraoperative and postoperative complications, and final aesthetic result.ResultsWe included 119 patients (102 men and 17 women) with a mean age of 71 years (range, 32–93 years). Mean follow-up was 42 months (range, 6–120 months). Sixty-eight patients had a moderate-sized defect with a distance between edges of 1 to 4 cm. Reconstructions started with relaxation incisions in 43 defects and resulted in the successful closure of 22 of them. Defects with a distance of 1 to 2 cm required a single relaxation incision. Two incisions were required for defects with a distance of 2 to 3 cm, while 3 incisions were required for those with a distance of 3 to 4 cm. In the 21 cases in which relaxation incisions were insufficient to close the defect, the incisions were extended to mobilize the flap to achieve closure. Relaxation incisions alone were insufficient for closing defects with a distance greater than 4 cm.ConclusionsThe 1-2-3 rule can help in choosing the best reconstruction technique for moderate-sized defects based on the principle that 1, 2, or 3 initial relaxation incisions are needed depending on the minimum distance between edges (1-2 cm, 2-3 cm, or 3-4 cm). In all cases, incision extension to mobilize the corresponding flaps remains an option.     

A Novel Algorithm to Differentiate Between Multiple Primary Lung Cancers and Intrapulmonary Metastasis in Multiple Lung Cancers With Multiple Pulmonary Sites of Involvement

IntroductionDifferentiating between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) is critical for developing a therapeutic strategy to treat multiple lung cancers with multiple pulmonary sites of involvement.MethodsWe retrospectively included 252 lesions (126 pairs) from 126 patients with surgically resected multiple lung adenocarcinomas. Each pair was classified as MPLC or IPM based on histopathologic findings as the reference standard. A novel algorithm was established with four sequential decision steps based on the combination of computed tomography (CT) lesion types (step 1), CT lesion morphology (step 2), difference of maximal standardized uptake values on positron-emission tomography/CT (step 3), and presence of N2/3 lymph node metastasis or distant metastasis (step 4). The diagnostic accuracy of the algorithm was analyzed. Performances of 11 observers were assessed without and with knowledge of algorithm.ResultsAmong 126 pairs, 90 (71.4%) were classified as MPLCs and 36 (28.6%) as IPMs. On applying the diagnostic algorithm, the overall accuracy for diagnosis of IPM among conclusive cases up to step 4 was 88.9%, and 65 and 44 pairs were correctly diagnosed based on step 1 and step 2, respectively. Specificity and positive predictive value for diagnosis of IPM increased significantly in all observers compared with reading rounds without the algorithm.ConclusionsApplication of the algorithm based on comprehensive information on clinical and imaging variables can allow differentiation between MPLCs and IPMs. When both of two suspected malignant lesions appear as solid predominant lesions without spiculation or air-bronchogram on CT, IPM should be considered.     

Acute GVHD: New approaches to clinical trial monitoring

Acute GVHD occurs in nearly 50% of patients receiving hematopoietic cell transplantation (HCT), and is the major driver of mortality. However, progress in the development of new acute GVHD therapeutics has been slow, in part due to heterogeneity in acute GVHD data collection and interpretation among centers. Herein, we first describe the methods used by the Mount Sinai Acute GVHD International Consortium (MAGIC) to standardize acute GVHD data collection and curation. We then review the utility of serum biomarkers, specifically the MAGIC Algorithm Probability (MAP) that combines two GI biomarkers (ST2 and REG3α) that has been shown to be more accurate than changes in clinical symptom severity after GVHD treatment. We then present preliminary data on the feasibility of a surrogate clinical trial endpoint that combines clinical response and MAP two weeks after treatment. This novel endpoint is an earlier and potentially better predictor of non-relapse mortality than the current gold standard of clinical response four weeks after treatment.     

Diagnosis and management of phyllodes tumours for the surgeon: An algorithm

A Phyllodes Tumour (PT) is an uncommon fibroepithelial lesion, with three histological grades – benign, borderline and malignant. PTs cause significant challenges in diagnosis, management and prognostication. Recent publications have clarified the definitions and prognostication of PTs. Contemporary data currently challenge international guidelines on PT management. We performed an in-depth literature review to develop a best-practice management algorithm for PTs.Diagnostic recommendations are that neither current imaging techniques, nor fine-needle biopsies, can reliably diagnose a PT. Core needle biopsy is the optimal diagnostic technique. Indeterminate or suspicious lesions are recommended to undergo an excisional biopsy due to the inherently heterogeneous nature of PTs.Management guidelines are that benign PTs should be completely excised, although an involved margin is acceptable in select situations. Borderline PTs should have a clear margin on excision due to their higher risk of recurrence, as well as the potential for a recurrence to progress to a malignant PT. In malignant PTs, a margin of 3 mm is acceptable as there is no reduction in recurrence risk if margins are >3 mm. Routine axillary surgery is not indicated in PTs, with axillary surgery only indicated in a histologically-confirmed positive axilla.Adjuvant treatment recommendations are that borderline and malignant PTs should be discussed at MDT, with radiotherapy considered in both. Chemotherapy should be discussed in malignant PT patients.In summary, we have developed an up-to-date simple algorithm to guide the surgeon's management of patients diagnosed with PTs and reduce excessive surgery.     

Seamless Integration of Artificial Intelligence Into the Clinical Environment: Our Experience With a Novel Pneumothorax Detection Artificial Intelligence Algorithm

Although interest in artificial intelligence (AI) has exploded in recent years and led to the development of numerous commercial and noncommercial algorithms, the process of implementing such tools into day-to-day clinical practice is rarely described in the burgeoning AI literature. In this report, we describe our experience with the successful integration of an AI-enabled mobile x-ray scanner with an FDA-approved algorithm for detecting pneumothoraces into an end-to-end solution capable of extracting, delivering, and prioritizing positive studies within our thoracic radiology clinical workflow. We also detail several sample cases from our AI algorithm and associated PACS workflow in action to highlight key insights from our experience. We hope this report can help inform other radiology enterprises seeking to evaluate and implement AI-related workflow solutions into daily clinical practice.