Bayesian Network Analysis for Prediction of Unplanned Hospital Readmissions of Cancer Patients with Breakthrough Cancer Pain and Complex Care Needs

Cascella, Marco; Racca, Emanuela; Nappi, Anna; Coluccia, Sergio; Maione, Sabatino; Luongo, Livio; Guida, Francesca; Avallone, Antonio; Cuomo, Arturo

doi:10.3390/healthcare10101853

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…It concerns the need to design differentiated pathways for distinct subgroups of patients. For example, in patients with gastrointestinal cancers and pain conditions, nutritional needs are the main cause of an increased number of unscheduled hospital admissions (14). Addressing neuropathic pain is usually a great challenge (15).…”

Section: Discussionmentioning

confidence: 99%

Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study

Cascella

Schiavo

Grizzuti

et al. 2023

In Vivo

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Background/Aim: Telemedicine, the remote delivery of healthcare services, represents a great opportunity for cancer pain management. A care model of telemedicine that combines remote visits and hospital access could be an effective and safe strategy for pain management of cancer patients. Patients and Methods: A retrospective study was conducted using the dataset of the telemedicine program at the Istituto Nazionale Tumori of Naples, Italy for assessing the efficacy of a telehealth-based model of care. Demographic, clinical, and process variables were investigated. Results: A total of 226 cases and 489 visits were included in the analysis. The mean age of patients was 63.4 years (SD=12.4 years), and no sex differences were observed. Approximately 55% of patients were ECOG-PS ≤2 and 87% suffered from metastatic disease. More than half of the patients were treated with high doses of opioids. Each patient had a mean of 2 remote visits and half of the patients had more than 1 telehealth consultation. The dropout ratio was 5.3%. Most visits (n=472) were conducted on patients in the Campania Region, Italy. The maximum covered distance from the Cancer Center and the patients' location was 555.22 Km. A significant difference in the overall number of visits (p=0.006) and the number of procapita remote visits (p=0.010) was found, in favor of the group of patients treated before the end of the Covid-19 emergency in Italy, compared to those treated after the pandemic. Conclusion: Despite various positive outcomes, the analysis highlights several weaknesses, such as the need to assist patients with advanced and non-advanced disease located outside the regional territory. Overall, the telehealth processes should be adapted to post-pandemic scenarios towards their implementation in routine clinical practice.

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Section: Discussionmentioning

confidence: 99%

Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study

Cascella

Schiavo

Grizzuti

et al. 2023

In Vivo

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“…This approach allows for an overview of the phenomenon through the evaluation of multiple variables. Notably, the dataset could be expanded and utilized for predictive analyses by using different data-driven prediction models [36].…”

Section: Study Limitationsmentioning

confidence: 99%

Management and Outcomes of Traumatic Liver Injury: A Retrospective Analysis from a Tertiary Care Center Experience

Alanezi,

Altoijry,

Alanazi

et al. 2024

Healthcare

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Background: although liver injuries are one of the most critical complications of abdominal trauma, choosing when to operate on these injuries is challenging for surgeons worldwide. Methods: We conducted a retrospective analysis of liver injury cases at our institution from 2016 to 2022 to describe the operative and nonoperative management (NOM) outcomes in patients with traumatic liver injuries. Baseline patient characteristics, liver injury details, treatments, and outcomes were analyzed. Results: Data from 45 patients (male, 77.8%) were analyzed. The mean age was 29.3 years. Blunt trauma was the most common injury mechanism (86.7%), whereas penetrating injuries were 8.9% of cases. Conservative management was associated with 18.9% of complications. The overall complication rate was 26.7%; delirium and sepsis were the most common (13.3%), followed by acute renal failure (4.4%), pneumonia, biliary leaks, and meningitis/seizures. Conclusions: Notwithstanding its limitations, this retrospective analysis demonstrated that NOM can serve as a safe and effective strategy for hemodynamically stable patients with liver trauma, irrespective of the patient’s injury grade. Nevertheless, careful patient selection and monitoring are crucial. Further investigations are necessary to thoroughly evaluate the management of traumatic liver injuries, particularly in the context of multiorgan injuries.

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“…Conversely, other areas, such as predicting postoperative complications in perioperative medicine, have not yet produced the desired results. Although many predictive models have been published, most are still in the research stage, and a valid and universally applicable intelligent tool for clinical practice has yet to be developed [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ].…”

Section: Clinical Practice and Research Perspectivesmentioning

confidence: 99%

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

et al. 2023

Self Cite

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Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by “ghosts”, which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.

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Bayesian Network Analysis for Prediction of Unplanned Hospital Readmissions of Cancer Patients with Breakthrough Cancer Pain and Complex Care Needs

Cited by 6 publications

References 40 publications

Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study

Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study

Management and Outcomes of Traumatic Liver Injury: A Retrospective Analysis from a Tertiary Care Center Experience

The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

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