Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers

Symbolic Dynamics Analysis: a new methodology for foetal heart rate variability analysis

Cardiotocography (CTG) is a widespread foetal diagnostic methods. However, it lacks of objectivity and reproducibility since its dependence on observer's expertise. To overcome these limitations, more objective methods for CTG interpretation have been proposed. In particular, many developed techniques aim to assess the foetal heart rate variability (FHRV). Among them, some methodologies from nonlinear systems theory have been applied to the study of FHRV. All the techniques have proved to be helpful in specific cases. Nevertheless, none of them is more reliable than the others. Therefore, an in-depth study is necessary. The aim of this work is to deepen the FHRV analysis through the Symbolic Dynamics Analysis (SDA), a nonlinear technique already successfully employed for HRV analysis. Thanks to its simplicity of interpretation, it could be a useful tool for clinicians. We performed a literature study involving about 200 references on HRV and FHRV analysis; approximately 100 works were focused on non-linear techniques. Then, in order to compare linear and non-linear methods, we carried out a multiparametric study. 580 antepartum recordings of healthy fetuses were examined. Signals were processed using an updated software for CTG analysis and a new developed software for generating simulated CTG traces. Finally, statistical tests and regression analyses were carried out for estimating relationships among extracted indexes and other clinical information. Results confirm that none of the employed techniques is more reliable than the others. Moreover, in agreement with the literature, each analysis should take into account two relevant parameters, the foetal status and the week of gestation. Regarding the SDA, results show its promising capabilities in FHRV analysis. It allows recognizing foetal status, gestation week and global variability of FHR signals, even better than other methods. Nevertheless, further studies, which should involve even pathological cases, are necessary to establish its reliability.La Cardiotocografia (CTG) è una diffusa tecnica di diagnostica fetale. Nonostante ciò, la sua interpretazione soffre di forte variabilità intra- e inter- osservatore. Per superare tali limiti, sono stati proposti più oggettivi metodi di analisi. Particolare attenzione è stata rivolta alla variabilità della frequenza cardiaca fetale (FHRV). Nel presente lavoro abbiamo suddiviso le tecniche di analisi della FHRV in tradizionali, o lineari, e meno convenzionali, o non-lineari. Tutte si sono rivelate efficaci in casi specifici ma nessuna si è dimostrata più utile delle altre. Pertanto, abbiamo ritenuto necessario effettuare un’indagine più dettagliata. In particolare, scopo della tesi è stato approfondire una specifica metodologia non-lineare, la Symbolic Dynamics Analysis (SDA), data la sua notevole semplicità di interpretazione che la renderebbe un potenziale strumento di ausilio all’attività clinica. Sono stati esaminati all’incirca 200 riferimenti bibliografici sull’analisi di HRV e FHRV; di questi, circa 100 articoli specificamente incentrati sulle tecniche non-lineari. E’ stata condotta un’analisi multiparametrica su 580 tracciati CTG di feti sani per confrontare le metodologie adottate. Sono stati realizzati due software, uno per l’analisi dei segnali CTG reali e l’altro per la generazione di tracciati CTG simulati. Infine, sono state effettuate analisi statistiche e di regressione per esaminare le correlazioni tra indici calcolati e parametri di interesse clinico. I risultati dimostrano che nessuno degli indici calcolati risulta più vantaggioso rispetto agli altri. Inoltre, in accordo con la letteratura, lo stato del feto e le settimane di gestazione sono parametri di riferimento da tenere sempre in considerazione per ogni analisi effettuata. Riguardo la SDA, essa risulta utile all’analisi della FHRV, permettendo di distinguere – meglio o al pari di altre tecniche – lo stato del feto, la settimana di gestazione e la variabilità complessiva del segnale. Tuttavia, sono necessari ulteriori studi, che includano anche casi di feti patologici, per confermare queste evidenze

Lean Six Sigma Approach to Implement a Femur Fracture Care Pathway at “San Giovanni di Dio e Ruggi d’Aragona” University Hospital

Timeliness in the treatment of fracture of the femur, through surgery, is crucial in the elderly patient as it reduces the risk of mortality and disability. Here we propose a Lean Six Sigma (LSS) approach to reduce the preoperative length of stay for patients with femur fracture. Through the LSS, a tailored Diagnostic Therapeutic Assistance Path (DTAP) for these has been implemented and monitored over time. In particular, through the analysis, based on the application of the DMAIC cycle conducted on data extrapolated from the information system of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno, the new DTAP was designed and implemented. After the introduction of the DTAP, a significant reduction in the average length of hospital stay was observed, with a preoperative length of stay within 48 h in 65% cases (compared to the previous 9%). In particular, the most significant reduction (over 55%) is obtained for patients aged over 65 years old. Such a result reflects not only the improvement in the care process but it is also compliant with the guidelines of the Italian Ministry of Health, as reported in the New Guarantee System for monitoring the quality of care. © 2021, Springer Nature Switzerland AG

Six Sigma Approach for a First Evaluation of a Pharmacological Therapy in Tongue Cancer

Tongue cancers are among the most frequent malignancies in the population and their influence can be affected by many risk factors. Patients undergoing tongue surgery face different complications and can experience a long length of hospital stay (LOS). The aim of this paper is to compare two pharmacological therapies in order to understand which one decreases the LOS. At the University hospital of Naples “Federico II” two antibiotics were employed: Cefazolin plus Clindamycin and Ceftriaxone. Six Sigma methodology was employed to analyse two group of patients treated with these two different antibiotics: 55 patients treated with the antibiotic Cefazolin plus Clindamycin and 66 patients with the antibiotic Ceftriaxone. This is the first time that this methodology is used in order to compare two antibiotics in the oncology field. The results obtained show clearly and with a statistical evidence that patients treated with Ceftriaxone experienced a lower LOS (−28.6% in terms of percentage between medians). Reducing the LOS for patients means limiting the number of complications and, therefore, reducing the hospitalization costs. It would be valuable for both hospital and patients: the former would save money that they could invest in other important care activities; the latter would experience a higher quality of care with fewer complications

Management of the Diabetic Patient in the Diagnostic Care Pathway

Diabetes is a complex pathology both for the affected patients and for the medical specialists who follow them. Furthermore, since diabetes is a pathology with a high prevalence and incidence, it is essential to intervene effectively in therapeutic actions through the application of common guidelines. Therefore, in order to improve the management of the diabetic patient, the aim of the work is to define a Diagnostic Therapeutic Assistance Pathway (PDTA). A questionnaire-based approach is adopted for data collection from 136 patients at the Clinical Dermatology Unit of the University Hospital “Federico II”. In most cases (64%) the diagnosis was made by the General Practitioner, 15% of patients obtained the diagnosis at the ASL and 12% at the Polyclinic of Naples AOU “Federico II” and the remaining part from the diabetologist specialist. The second access is generally carried out at the “Federico II” AOU (66%), followed by the ASL (17%), by a doctor specialized in diabetology (12%) while no patient has turned to the General Practitioner for the treatment of diabetes. The final visit is carried out at the “Federico II” AOU in almost cases. The data obtained follow the Italian guidelines: the patients get the diagnosis from the Family Doctor and then they are addressed either to ASL or to diabetologists specialists. For the subsequent visits, most of them prefer to turn to the “Federico II” AOU, especially when they have complications associated with the diseases as they are followed in a more careful and satisfying manner

Application of Supply Chain Management at Drugs Flow in an Italian Hospital District

The globalization has pushed to change the organization of every companies, even the hospitals. The principal phenomenon in that period and fundamental today again, has been the Supply Chain Management (SCM), with which the company is no longer seen as an isolated entity but active part in an extremely complex supply network. In fact, the only way to guarantee the competitiveness of businesses in the new world economy is through the cooperation and the integration between customers and suppliers. The present work analyses the drugs flow of three Italian hospital: the Cardarelli Hospital in Campobasso, the Veneziale located in Isernia and the San Timoteo site in Termoli. The data was provided by MOLISE DATA SPA that collected the information from all ASREM with particular interest in the already mentioned hospitals. Particularly, will be highlight, using simulation model, the benefits deriving from the implementation of a new Supply Chain, creating a collaboration along the entire logistics production chain. Thanks to a more efficient management of drugs will get a reduction of business costs and an improvement of the health services offered

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study

Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy)

A Health Technology Assessment in Maxillofacial Cancer Surgery by Using the Six Sigma Methodology

Squamous cell carcinoma represents the most common cancer affecting the oral cavity. At the University of Naples “Federico II”, two different antibiotic protocols were used in patients undergoing oral mucosa cancer surgery from 2006 to 2018. From 2011, there was a shift; the combination of Cefazolin plus Clindamycin as a postoperative prophylactic protocol was chosen. In this paper, a health technology assessment (HTA) is performed by using the Six Sigma and DMAIC (Define, Measure, Analyse, Improve, Control) cycle in order to compare the performance of the antibiotic protocols according to the length of hospital stay (LOS). The data (13 variables) of two groups were collected and analysed; overall, 136 patients were involved. The American Society of Anaesthesiologist score, use of lymphadenectomy or tracheotomy and the presence of infections influenced LOS significantly (p-value < 0.05) in both groups. Then, the groups were compared: the overall difference between LOS of the groups was not statistically significant, but some insights were provided by comparing the LOS of the groups according to each variable. In conclusion, in light of the insights provided by this study regarding the comparison of two antibiotic protocols, the utilization of DMAIC cycle and Six Sigma tools to perform HTA studies could be considered in future research

Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network

Background: Electronic fetal monitoring (EFM) is widely applied as a routine diagnostic tool by clinicians using fetal heart rate (FHR) signals to prevent fetal hypoxia. However, visual interpretation of the FHR usually leads to significant inter-observer and intra-observer variability, and false positives become the main cause of unnecessary cesarean sections.Goal: The main aim of this study was to ensure a novel, consistent, robust, and effective model for fetal hypoxia detection.Methods: In this work, we proposed a novel computer-aided diagnosis (CAD) system integrated with an advanced deep learning (DL) algorithm. For a 1-dimensional preprocessed FHR signal, the 2-dimensional image was transformed using recurrence plot (RP), which is considered to greatly capture the non-linear characteristics. The ultimate image dataset was enriched by changing several parameters of the RP and was then used to feed the convolutional neural network (CNN). Compared to conventional machine learning (ML) methods, a CNN can self-learn useful features from the input data and does not perform complex manual feature engineering (i.e., feature extraction and selection).Results: Finally, according to the optimization experiment, the CNN model obtained the average performance using optimal configuration across 10-fold: accuracy = 98.69%, sensitivity = 99.29%, specificity = 98.10%, and area under the curve = 98.70%.Conclusion: To the best of our knowledge, this approached achieved better classification performance in predicting fetal hypoxia using FHR signals compared to the other state-of-the-art works.Significance: In summary, the satisfied result proved the effectiveness of our proposed CAD system for assisting obstetricians making objective and accurate medical decisions based on RP and powerful CNN algorithm