Leveraging Artificial Intelligence and Machine Learning to Optimize Enhanced Recovery After Surgery (ERAS) Protocols

Enhanced recovery after surgery (ERAS) protocols have transformed perioperative care by implementing evidence-based strategies to hasten patient recovery, decrease complications, and shorten hospital stays. However, challenges such as inconsistent adherence and the need for personalized adjustments persist, prompting exploration into innovative solutions. The emergence of artificial intelligence (AI) and machine learning (ML) offers a promising avenue for optimizing ERAS protocols. While ERAS emphasizes preoperative optimization, minimally invasive surgery (MIS), and standardized postoperative care, challenges such as adherence variability and resource constraints impede its effectiveness. AI/ML technologies offer opportunities to overcome these challenges by enabling real-time risk prediction, personalized interventions, and efficient resource allocation. AI/ML applications in ERAS extend to patient risk stratification, personalized care plans, and outcome prediction. By analyzing extensive patient datasets, AI/ML algorithms can predict individual patient risks and tailor interventions accordingly. Moreover, AI/ML facilitates proactive interventions through predictive modeling of postoperative outcomes, optimizing resource allocation, and enhancing patient care. Despite the potential benefits, integrating AI and ML into ERAS protocols faces obstacles such as data access, ethical considerations, and healthcare professional training. Overcoming these challenges requires a human-centered approach, fostering collaboration among clinicians, data scientists, and patients. Transparent communication, robust cybersecurity measures, and ethical model validation are crucial for successful integration. It is essential to ensure that AI and ML complement rather than replace human expertise, with clinicians maintaining oversight and accountability.

From Nanobots to Neural Networks: Multifaceted Revolution of Artificial Intelligence in Surgical Medicine and Therapeutics

This comprehensive exploration unveils the transformative potential of Artificial Intelligence (AI) within medicine and surgery. Through a meticulous journey, we examine AI's current applications in healthcare, including medical diagnostics, surgical procedures, and advanced therapeutics. Delving into the theoretical foundations of AI, encompassing machine learning, deep learning, and Natural Language Processing (NLP), we illuminate the critical underpinnings supporting AI's integration into healthcare. Highlighting the symbiotic relationship between humans and machines, we emphasize how AI augments clinical capabilities without supplanting the irreplaceable human touch in healthcare delivery. Also, we'd like to briefly mention critical findings and takeaways they can expect to encounter in the article. A thoughtful analysis of the economic, societal, and ethical implications of AI's integration into healthcare underscores our commitment to addressing critical issues, such as data privacy, algorithmic transparency, and equitable access to AI-driven healthcare services. As we contemplate the future landscape, we project an exciting vista where more sophisticated AI algorithms and real-time surgical visualizations redefine the boundaries of medical achievement. While acknowledging the limitations of the present research, we shed light on AI's pivotal role in enhancing patient engagement, education, and data security within the burgeoning realm of AI-driven healthcare.

Oncological Horizons: The Synergy of Medical and Surgical Innovations in Cancer Treatment

The landscape of cancer treatment has witnessed a remarkable transformation in recent years, marked by the convergence of medical and surgical innovations. Historically, cancer therapy faced challenges, including limited efficacy and severe side effects. This narrative review explores the historical progression of cancer treatments, shedding light on significant breakthroughs in both medical and surgical oncology. It comprehensively addresses the medical domain, covering chemotherapy, targeted therapies, immunotherapy, hormonal treatments, and radiological procedures. Simultaneously, it delves into the surgical realm, discussing the evolution of surgical techniques, minimally invasive procedures, and the role of surgery across various stages of cancer. The article emphasizes the fusion of medical and surgical approaches, highlighting neoadjuvant and adjuvant therapies and the significance of multidisciplinary tumor boards. It also addresses innovations, challenges, and the pivotal role of patient-centered care. Furthermore, it offers insights into the future directions and forecasts in the constantly evolving field of integrated oncological care. This review provides a comprehensive understanding of the dynamic and transformative nature of cancer treatment, reflecting the unwavering commitment of the medical and surgical communities in the ongoing fight against cancer.

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

BACKGROUND

Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.

METHODS

We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).

FINDINGS

In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).

INTERPRETATION

In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.

FUNDING

British Journal of Surgery Society.

372 Efficacy of Enhanced Figure of 8 Percutaneous Repair of Failed Closed Soft Tissue Mallet Finger

Introduction

Chronic tendinous mallet finger injuries are commonly treated with an open operative fixation.

Aim

To evaluate a new technique of closed reduction and fixation using a percutaneous figure of 8 suture for treating closed tendinous mallet finger injury.

Method

We conducted a retrospective analysis of prospectively collected data of 8 consecutive patients, undergoing percutaneous fixation of chronic mallet finger injuries. All patients had failed conservative splinting of duration 10.5 weeks (9–12 weeks) with a residual extensor lag of the distal interphalangeal joint greater than 30 degrees. Surgery was performed using a minimally invasive technique using a 3–0 proline suture in a percutaneous figure of 8 pattern, by going through the periosteum on both sides and providing rigid hyperextension. Patients were kept in a splint for 7 days followed by full mobilisation under supervision of hand therapists.

Results

The average age of our cohort was 51 (40–62 years old). At an average of 6.2 months All patients achieved correction of deformity to less than10 degrees. The overall functional outcome was good to excellent in all patients with no incidence of recurrence at last follow up. There was no instance of skin necrosis or wound complications in any of the patients.

Conclusions

The new figure of 8 percutaneous technique is an effective, safe, and minimally invasive approach for closed tendinous mallet finger injures. This needs further validation by analysing outcomes in a larger cohort.