Analysis of variance

Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis

BACKGROUND

The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status.

PURPOSE

To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer.

MATERIAL AND METHODS

We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared.

RESULTS

The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model.

CONCLUSION

The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches.

Validation of the Helsinki University Hospital prevent pressure Injury Risk Assessment Tool: a prospective observational study

Background

Pressure injures are a common adverse event in a hospital, and they are one of the most important quality indicators of patient care. Risk assessment is recommended as the first step in the prevention of pressure injuries. A Prevent Pressure Injury Risk Assessment Tool is a new tool for risk assessment that was developed by the Helsinki University Hospital.

Aim

The aim of this study was to evaluate the predictive validity and the concurrent validity of the Prevent Pressure Injury Risk Assessment Tool in acute care.

Method

The prospective observational study was conducted in 19 in-patient wards representing internal medicine, neurology, and surgery during 2017–2018. The participants’ inclusion criteria were: age ≥18 years old, no pressure injury on admission to the hospital and consenting to participate. The data collected by physical assessment of patients was combined with data from electronic patient records. Each patient was assessed by two different nurses with the Prevent Pressure Injury Risk Assessment Tool and the Braden Scale at patient admission. Furthermore, skin condition was observed throughout the hospital stay.

Results

Of the 637 patients accepted for the study, 10 (1.6%) developed a pressure injury during the hospital stay. Poisson regression analysis showed that pressure injuries were more likely in high–risk patients compared to those with low-risk. The sensitivity of the Prevent Pressure Injury Risk Assessment Tool was adequate (75%), while specificity was poor (40%). A moderate correlation was found between the Prevent Pressure Injury Risk Assessment Tool and the Braden Scale.

Conclusions

The Prevent Pressure Injury Risk Assessment Tool may be useful for identifying the adult pressure injury risk patients in acute care. Further research is needed to evaluate interrater reliability, and usability and validity with different patient populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-021-00799-6.

A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.