Attitude toward second opinions in Germany – a survey of the general population

Background Second medical opinions (SOs) can assist patients in making informed treatment decisions and improve the understanding of their diagnosis. In Germany, there are different approaches to obtain a structured SO procedure: SO programs by health insurers and SOs according to the SO Directive. Through a direct survey of the population, we aimed to assess how structured SOs should be provided to fulfil patients’ needs. Methods A stratified sample of 9990 adults (≥18 years) living in the federal states of Berlin and Brandenburg (Germany) were initially contacted by post in April and sent a reminder in May 2020. The survey results were analyzed descriptively. Results Among 1349 participants (response rate 14%), 56% were female and the median age was 58 years (interquartile range (IQR) 44–69). Participants wanted to be informed directly and personally about the possibility of obtaining an SO (89%; 1201/1349). They preferred to be informed by their physician (93%; 1249/1349). A majority of participants would consider it important to obtain an SO for oncological indications (78%; 1049/1349). Only a subset of the participants would seek an SO via their health insurer or via an online portal (43%; 577/1349 and 16%; 221/1349). A personally delivered SO was the preferred route of SO delivery, as 97% (1305/1349) would (tend to) consider this way of obtaining an SO. Participants were asked to imagine having moderate knee pain for years, resulting in a treatment recommendation for knee joint replacement. They were requested to rate potential qualification criteria for a physician providing the SO. The criteria rated to be most important were experience with the recommended diagnosis/treatment (criterion (very) important for 93%; 1257/1349) and knowledge of the current state of research (criterion (very) important for 86%; 1158/1349). Participants were willing to travel 60 min (median; IQR 60–120) and wait 4 weeks (median; IQR 2–4) for their SO in the hypothetical case of knee pain. Conclusion In general, SOs were viewed positively. We found that participants have clear preferences regarding SOs. We propose that these preferences should be taken into account in the future design and development of SO programs.

Surgical treatment of the bony mallet thumb: a case series and literature review

Introduction The aim of this study was to provide a more precise statement on the outcome after surgical treatment of a bony mallet thumb and possibly give a treatment recommendation regarding the surgical fixation method. Patients and methods All patients (n = 16) who underwent a surgical treatment for an acute bony mallet thumb fracture between January 2006 and July 2019 were enrolled. The surgical method, complications, the range of motion, tip pinch, lateral key pinch, overall grip strength, visual analog score, Disability of the Arm, Shoulder and Hand Score, Mayo Wrist Score, Patient-Rated Wrist Evaluation Score, Buck-Gramcko Score and radiologic parameters were evaluated. Further, a comprehensive literature search on PubMed was conducted covering a period from 1956 to 2021 to include all possible matching articles on the treatment of the bony mallet thumb (n = 21 articles). Results Surgical treatment was very inhomogenous including indirect and direct K-wire fixation, screw fixation, plate fixation and anchor fixation methods. The IP joint range of motion and thumb strength ranged from 66 to 94% in comparison to the contralateral side. An open reduction led to worse functional scores compared to a closed reduction. Treatment methods in the literature were also very inhomogenous with a very low patient count, often even pooling data of bony mallet thumb fractures with bony mallet finger fractures. The risk for infection was higher in K-wire fixation methods than in open reduction and internal fixation methods. Conclusion The evidence for the best treatment of a bony mallet thumb fracture is low. On one hand the functional outcome can be inferior using an open reduction approach, but on the other hand, K-wire fixation methods with a closed reduction approach showed a higher risk for infection. Future multi-center research must be conducted to find the best treatment procedure for the best outcome of the patient.

Predictors for Adherence to Treatment Strategies in Elderly HNSCC Patients

Finding a cure may be less important than ensuring the quality of life in elderly patients with head and neck squamous cell carcinoma (HNSCC). The aim of this study was to determine predictors for adherence. Clinical and pathological data from patients ≥70 years with HNSCC (initial diagnoses 2004-2018) were investigated retrospectively. Evaluated clinical predictors included biological age (Charlson Comorbidity Index; CCI), patient health (Karnofsky Performance Status; KPS) and therapy data. A total of 1125 patients were included. The median age was 75 years, 33.1% reached CCI ≥6, and 53.7% reached KPS ≤ 70%. In total, 968 patients were adherent, whereas 157 were nonadherent. Nonadherent patients were significantly more often smokers (p = 0.003), frequent drinkers (p = 0.001), had a worse health status (p ≤ 0.001) and a lower biological age (p = 0.003), an advanced T classification and lymph node involvement or UICC stage (each p ≤ 0.001). Approximately 88.0% of the included patients received a curative treatment recommendation. A total of 6.9% discontinued the therapy, and 7.0% refused the therapy. With the increasing complexity of a recommended therapy, adherence decreased. The 5-year overall survival was significantly higher in adherent patients (45.1% versus 19.2%). In contrast to the chronological patient age, biological age is a significant predictor for adherence. The evaluated predictors for nonadherence need to be verified prospectively.

A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

Guideline Adherence and Implementation of Tumor Board Therapy Recommendations for Patients With Gastrointestinal Cancer

Purpose: Although participation in multidisciplinary tumor boards (MTBs) is an obligatory quality criterion for certification, there is scarce evidence, whether MTB recommendations are consistent with consensus guidelines and whether they are followed in clinical practice. Reasons of guideline and tumor board deviations are poorly understood so far. Methods: MTBs recommendations from the weekly MTB for gastrointestinal cancers at the University Cancer Center Leipzig/Germany (UCCL) in 2020 were analyzed for their adherence to therapy recommendations as stated in National German guidelines and implementation within an observation period of 3 months. To assess adherence, an objective classification system was developed assigning a degree of guideline and tumor board adherence to each MTB case. For cases with deviations, underlying causes and influencing factors were investigated and categorized. Results: 76% of MTBs were fully adherent to guidelines, with 16% showing deviations, mainly due to study inclusions and patient comorbidities. Guideline adherence in 8% of case discussions could not be determined, especially because there was no underlying guideline recommendation for the specific topic. Full implementation of the MTBs treatment recommendation occurred in 64% of all cases, while 21% showed deviations with primarily reasons of comorbidities and differing patient wishes. Significantly lower guideline and tumor board adherences were demonstrated in patients with reduced performance status (ECOG-PS ≥ 2) and for palliative intended therapy (p=.002/.007). Conclusion: The assessment of guideline deviations and adherence to MTB decisions by a systematic and objective quality assessment tool could become a meaningful quality criterion for cancer centers in Germany.

Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning

Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model’s recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications.

MEDIATION ANALYSIS TO INFORM POLICY ON CORONARY REVASCULARIZATION BY EXPECTED TIME TO TREATMENT: ANALYTICAL FRAMEWORK

Objectives Clinical guidelines favor coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI) for patients with stable complex coronary disease. Yet the benefit of CABG as established in trials may not be generalizable to populations in which treatment method determines time to treatment, typically being longer for CABG. For cases in which the cardiac anatomy is suitable for either treatment, it is unclear whether it is appropriate to recommend CABG, which is likely to be delayed, if PCI can be performed sooner. This paper outlines an analytical framework for a policy analysis of the timing of coronary revascularization. Methods We constructed a thought experiment to examine whether time to treatment will influence the advantage of CABG. We substantiated the use of mediation analysis to estimate the extent to which differences in outcomes between CABG and PCI would change if times to CABG were the same as times to PCI. Results We designed a study that uses data from a population-based patient registry to obtain effect measures of mediation analysis: the total effect, the natural indirect effect, and the natural direct effect. The partitioning of the total effect will allow us to estimate the proportional reduction in the risk of an outcome if the time to CABG was similar to that of PCI. Interpretation Treatment recommendation, resource allocation and scheduling benchmarks will be guided by understanding the extent to which the time to treatment mediates the relation between revascularization method and outcome.

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein, the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data.

The Influence of Heel Height on Strain Variation of Plantar Fascia During High Heel Shoes Walking-Combined Musculoskeletal Modeling and Finite Element Analysis

The therapeutic benefit of high heel shoes (HHS) for plantar fasciitis treatment is controversial. It has been suggested that plantar fascia strain can be decreased by heel elevation of shoes which helps in body weight redistribution throughout the length of the foot. Yet it is a fact that the repetitive tension caused by HHS wearing resulting in plantar fasciitis is a high-risk disease in HHS individuals who suffer heel and plantar pain. To explore the biomechanical function on plantar fascia under HHS conditions, in this study, musculoskeletal modeling (MsM) and finite element method (FEM) were used to investigate the effect of heel height on strain distribution of plantar fascia. Three-dimensional (3D) and one-dimensional (1D) finite element models of plantar fascia were generated to analyze the computed strain variation in 3-, 5-, and 7-cm heel heights. For validation, the computed foot contact pressure was compared with experimental measurement, and the strain value on 1D fascia was compared with previous studies. Results showed that the peak strain of plantar fascia was progressively increased on both 3D and 1D plantar fascia as heel elevated from 3 to 7 cm, and the maximum strain of plantar fascia occurs near the heel pain site at second peak stance. The 3D fascia model predicted a higher strain magnitude than that of 1D and provided a more reliable strain distribution on the plantar fascia. It is concluded that HHS with narrow heel support could pose a high risk on plantar fasciitis development, rather than reducing symptoms. Therefore, the heel elevation as a treatment recommendation for plantar fasciitis is questionable. Further studies of different heel support structures of shoes to quantify the effectiveness of heel elevation on the load-bearing mechanism of plantar fascia are recommended.

LUMOS - Low and Intermediate Grade Glioma Umbrella Study of Molecular Guided TherapieS at relapse: Protocol for a pilot study

IntroductionGrades 2 and 3 gliomas (G2/3 gliomas), when combined, are the second largest group of malignant brain tumours in adults. The outcomes for G2/3 gliomas at progression approach the dismal outcomes for glioblastoma (GBM), yet there is a paucity of trials for Australian patients with relapsed G2/3 gliomas compared with patients with GBM. LUMOS will be a pilot umbrella study for patients with relapsed G2/3 gliomas that aims to match patients to targeted therapies based on molecular screening with contemporaneous tumour tissue. Participants in whom no actionable or no druggable mutation is found, or in whom the matching drug is not available, will form a comparator arm and receive standard of care chemotherapy. The objective of the LUMOS trial is to assess the feasibility of this approach in a multicentre study across five sites in Australia, with a view to establishing a national molecular screening platform for patient treatment guided by the mutational analysis of contemporaneous tissue biopsiesMethods and analysisThis study will be a multicentre pilot study enrolling patients with recurrent grade 2/3 gliomas that have previously been treated with radiotherapy and chemotherapy at diagnosis or at first relapse. Contemporaneous tumour tissue at the time of first relapse, defined as tissue obtained within 6 months of relapse and without subsequent intervening therapy, will be obtained from patients. Molecular screening will be performed by targeted next-generation sequencing at the reference laboratory (PathWest, Perth, Australia). RNA and DNA will be extracted from representative formalin-fixed paraffin embedded tissue scrolls or microdissected from sections on glass slides tissue sections following a review of the histology by pathologists. Extracted nucleic acid will be quantified by Qubit Fluorometric Quantitation (Thermo Fisher Scientific). Library preparation and targeted capture will be performed using the TruSight Tumor 170 (TST170) kit and samples sequenced on NextSeq 550 (Illumina) using NextSeq V.2.5 hi output reagents, according to the manufacturer’s instructions. Data analysis will be performed using the Illumina BaseSpace TST170 app v1.02 and a custom tertiary pipeline, implemented within the Clinical Genomics Workspace software platform from PierianDx (also refer to section 3.2). Primary outcomes for the study will be the number of patients enrolled and the number of patients who complete molecular screening. Secondary outcomes will include the proportion of screened patients enrolled; proportion of patients who complete molecular screening; the turn-around time of molecular screening; and the value of a brain tumour specific multi-disciplinary tumour board, called the molecular tumour advisory panel as measured by the proportion of patients in whom the treatment recommendation was refined compared with the recommendations from the automated bioinformatics platform of the reference laboratory testing.Ethics and disseminationThe study was approved by the lead Human Research Ethics Committee of the Sydney Local Health District: Protocol No. X19-0383. The study will be conducted in accordance with the principles of the Declaration of Helsinki 2013, guidelines for Good Clinical Practice and the National Health and Medical Research Council National Statement on Ethical Conduct in Human Research (2007, updated 2018 and as amended periodically). Results will be disseminated using a range of media channels including newsletters, social media, scientific conferences and peer-reviewed publications.Trial registration numberACTRN12620000087954; Pre-results.