Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

Building on the success of anti-vascular endothelial growth factor therapy: a vision for the next decade

This article aims to identify key opportunities for improvement in the diagnosis and treatment of retinal disease, and describe recent innovations that will potentially facilitate improved outcomes with existing intravitreal vascular endothelial growth factor (VEGF) therapies and lay the groundwork for new treatment approaches. The review begins with a summary of the key discoveries that led to the development of anti-VEGF therapies and briefly reviews their impact on clinical practice. Opportunities for improvements in diagnosis, real-world outcomes with existing therapies, long-acting therapeutics and personalised health care are discussed, as well as the need to identify new targets for therapeutic intervention. Low-cost, remote patient screening and monitoring using artificial intelligence (AI)-based technologies can help improve diagnosis rates and enable remote disease monitoring with minimal patient burden. AI-based tools can be applied to generate patient-level prognostic data and predict individual treatment needs, reducing the time needed to optimise a patient’s treatment regimen. Long-acting therapeutics can help improve visual outcomes by reducing the treatment burden. When paired with AI-generated prognoses, long-acting therapeutics enable the possibility of vision loss prevention. Dual-acting drugs may help improve efficacy and/or durability beyond what is possible with anti-VEGF agents alone. Recent developments and ongoing innovations will help build upon the success of anti-VEGF therapies to further reduce vision loss owing to retinal disease while lowering the overall burden of care.Subject terms: Retinal diseases, Quality of life     

Neurocritical Care Resource Utilization in Pandemics: A Statement by the Neurocritical Care Society

Neurocritical Care -     

Molecular Docking Studies of Phytocompounds from the Phyllanthus Species as Potential Chronic Pain Modulators

The study of inflammatory pain has been one of the most rapidly advancing and expanding areas of pain research in recent years. Studies from our lab have demonstrated the chronic pain-modulating potential of the Phyllanthus species and their probable interaction with various inflammatory mediators involving enzymes like COX-2 and PGE synthase, cytokines like TNF-alpha and IL-1 beta, and with the NMDA receptor. Inflammatory mediators which play a crucial role in chronic inflammatory hyperalgesia and its subsequent modulation were selected for their interactions with 86 structurally diverse phytoconstituents identified from the Phyllanthus species.The docking analysis of the target proteins with the phytochemical ligands was performed using VLifeMDS software. The docking scores and analysis of the interactions of the phytocompounds with target proteins suggest that important molecules like lupeol, phyllanthin, hypopyllanthin, corilagin, epicatechin, and most of the other compounds have the ability to bind to multiple targets involved in inflammatory hyperalgesia.Our study strongly suggests that the findings of the present study could be exploited in the future for designing ligands in order to obtain novel molecules for the treatment and management of chronic pain.     

Paraneoplastic cerebellar degeneration as a presenting manifestation of non-Hodgkin’s lymphoma

Background

Paraneoplastic Cerebellar degeneration (PCD) is one of the classical paraneoplastic syndromes (PNS) which is characterised by subacute onset, progressive cerebellar ataxia and is usually associated with small cell lung carcinoma, adeno carcinoma of breast and ovary followed by Hodgkin’s lymphoma.

Objective

We herein report a case of subacute onset, progressive cerebellar ataxia in a 37-year-old female, who on evaluation was found to have non-Hodgkin’s lymphoma and experienced good clinical response to treatment.

Discussion

As compared to solid tumours, chances of association of PNS with Lymphomas is quite low and there are only few case reports in the literature showing association of PCD with non-Hodgkin’s lymphoma. As PCD is one of the classical PNS, it is very important to identify subtle cerebellar manifestations in an otherwise apparently normal individual, as early diagnosis and aggressive treatment can immensely improve the mortality and morbidity associated with this syndrome.

Conclusion

This case signifies the importance of suspecting PNS as an important differential diagnosis in a young patient presenting with subacute onset progressive cerebellar ataxia and evaluating her extensively for malignancy in spite of no paraneoplastic antibody been detected as early diagnosis and treatment can lead to gratifying response. We do agree that 2 weeks follow up is a short time interval to determine whether the response was sustained or not, for which a long term follow up is required.

     

Alexander fractional differential window filter for ECG denoising

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart. During the process of recording and transmission, ECG signals are often corrupted by various types of noises. Minimizations of these noises facilitate accurate detection of various anomalies. In the present paper, Alexander fractional differential window (AFDW) filter is proposed for ECG signal denoising. The designed filter is based on the concept of generalized Alexander polynomial and the R–L differential equation of fractional calculus. This concept is utilized to formulate a window that acts as a forward filter. Thereafter, the backward filter is constructed by reversing the coefficients of the forward filter. The proposed AFDW filter is then obtained by averaging of the forward and backward filter coefficients. The performance of the designed AFDW filter is validated by adding the various type of noise to the original ECG signal obtained from MIT-BIH arrhythmia database. The two non-diagnostic measure, i.e., SNR, MSE, and one diagnostic measure, i.e., wavelet energy based diagnostic distortion (WEDD) have been employed for the quantitative evaluation of the designed filter. Extensive experimentations on all the 48-records of MIT-BIH arrhythmia database resulted in average SNR of 22.014?±?3.806365, 14.703?±?3.790275, 13.3183?±?3.748230; average MSE of 0.001458?±?0.00028, 0.0078?±?0.000319, 0.01061?±?0.000472; and average WEDD value of 0.020169?±?0.01306, 0.1207?±?0.061272, 0.1432?±?0.073588, for ECG signal contaminated by the power line, random, and the white Gaussian noise respectively. A new metric named as morphological power preservation measure (MPPM) is also proposed that account for the power preservance (as indicated by PSD plots) and the QRS morphology. The proposed AFDW filter retained much of the original (clean) signal power without any significant morphological distortion as validated by MPPM measure that were 0.0126, 0.08493, and 0.10336 for the ECG signal corrupted by the different type of noises. The versatility of the proposed AFDW filter is also validated by its application on the ECG signal from MIT-BIH database corrupted by the combination of the noises as well as on the real noisy ECG signals are taken from MIT-BIH ID database. Furthermore, the comparative study has also been done between the proposed AFDW filter and existing state of the art denoising algorithms. The results clearly prove the supremacy of our proposed AFDW filter.