Patient Safety in Surgery最新文献

Background: Precise estimates of risk-adjusted increases in postoperative length of stay (LOS) associated with postoperative complications across a range of complications and operations are not available in the existing literature.

Methods: Associations between preoperative characteristics, postoperative complications and postoperative LOS were tested using medians, interquartile ranges, and nonparametric rank sum tests in a retrospective cohort study using the 2005-2018 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) dataset. A negative binomial model was used with postoperative LOS as the dependent variable and preoperative characteristics and postoperative complications as independent variables. The model was applied to estimate each patient's postoperative LOS with and without each postoperative complication to measure the association between each complication and risk-adjusted change in postoperative LOS.

Results: A total of 4,495,582 patients were included. After risk-adjustment, occurrence of each postoperative complication was associated with significantly increased postoperative LOS (between + 3.9 and + 20.1 days, p < 0.0001). The longest risk-adjusted postoperative LOS increases were associated with prolonged ventilator use (+ 20.1 days), wound disruption (+ 19.4 days), and acute renal failure (+ 17.1 days).

Conclusion: Occurrence of any postoperative complication was associated with increased risk-adjusted postoperative LOS. Degree of increase varied by complication. These data could be useful for patient counseling, allocation of resources, discharge planning, and quality improvement efforts.

Background: The management of hemodynamically unstable pelvic ring injuries necessitates surgical intervention, often involving procedures such as external fixation and percutaneous screw placement. Given the infrequent performance of these procedures, regular training is imperative to ensure readiness for emergencies. Our pre- post simulation study aimed to adapt and validate a realistic simulation model for stabilizing unstable pelvic ring injuries, facilitating participants' knowledge retention and procedural confidence enhancement.

Methods: A standardized simulator of an unstable pelvic ring utilizing synthetic pelvic bones featuring complete disruption of the symphysis and sacroiliac joint was developed. Trauma surgeons of a level one academic hospital were invited to perform external fixation and emergency sacroiliac screw application under C-arm guidance. Prior to and following the simulation session, participants completed a subjective questionnaire assessing their confidence in emergency interventions on a 10-point Likert scale (10-LS). Objective parameters, such as intraoperative imaging quality, reduction accuracy, and the positioning of screws, wires, and external fixators, were also evaluated as secondary outcome measures.

Results: Fifteen trauma surgeons (10 residents, 5 consultants) participated in the simulation over the course of one day. The mean total operation time was 20.34 ± 6.06 min, without significant differences between consultants and residents (p = 0.604). The confidence for emergency SI-Screw placement increased significantly after the simulator (10-LS: Before = 3.8 ± 3.08 vs. After = 5.67 ± 2.35; p = 0.002) as well as after external fixation (10-LS: Before = 3.93 ± 2.79 vs. After = 6.07 ± 2.52; p = 0.002). In addition, confidence in (intraoperative) pelvic imaging increased significantly (10-LS: Before = 4.60 ± 3.0 vs. After = 6.53 ± 2.39; p = 0.011). Overall, the model was rated as a realistic simulation of clinical practice (10-LS = 7.87 ± 1.13).

Conclusions: Our unstable pelvis fracture model is a tool to practice emergency interventions such as external fixation and percutaneous techniques. Participants benefitted from this in terms of technical instrumentation as well as intraoperative imaging. Further studies are required to validate the objective benefits and improvements that participants undergo through frequent training.

Background: This study aims to evaluate the efficacy of an oxygen scavenging mask device in reducing local oxygen concentrations from nasal cannula ventilation compared to a standard open facial surgical field.

Methods: This is a controlled experiment using a custom-fabricated silicone midfacial oxygen scavenging device, SimMan airway management trainer manikin (Laerdal Medical, Stavanger, Norway), handheld oxygen detector (Forensics Detectors, Los Angeles, United States) and oxygen from a Datex Ohmeda Aisys Carestation anesthesia unit (GE HealthCare, Chicago, United States). Oxygen concentrations were measured at 18 facial landmarks (Fig. 1) with nasal cannula flow of 2, 4, and 6 L/min of 100% FiO2 in both masked and unmasked conditions (Fig. 2).

Results: The mean oxygen concentration in the facial surgical field was 20.95% with the scavenger mask and 24.8% without (P < 0.001; two-tailed paired t-test). The unmasked condition was associated with suprathreshold oxygen concentration levels at 13 of 18 facial landmarks (Table 1). The device significantly reduced local oxygen concentration at 16 of 18 facial landmarks (Table 1). The device provided safe oxygen concentration levels at all three flow rates, and measured oxygen concentrations directly correlated with oxygen flow rate in the unmasked condition (Table 2).

Conclusions: An oxygen scavenger mask device reduced local oxygen concentrations from nasal cannula ventilation to below the 23% fire threshold in the entire facial surgical field external to the mask in these experiments. The device may reduce intraoperative fire risk in patients that require supplementary oxygen during surgery.

Introduction: This case report reports an unusual occurrence of gossypiboma, which refers to the accidental retention of surgical materials like sponges in the peritoneal cavity. The term is derived from "gossypium" (cotton) and "boma" (place of concealment). Its incidence varies with surgical type, posing diagnostic challenges due to nonspecific symptoms and equivocal imaging. Despite its rarity, gossypiboma poses significant risks, including intestinal obstruction and abscess formation.

Case presentation: A 37-year-old woman with ten previous pregnancies and an emergent caesarean section presented with abdominal pain. Examination and ultrasound suggested an ovarian cyst. During surgery, a 10 × 10 cm gauze-filled mass adherent to the ovary and jejunum was found. Postoperatively, she recovered well with no complications. The patient was treated with intravenous fluids and antibiotics for five days post-surgery and recovered without any complications. She was discharged from the hospital five days after the procedure.

Conclusion: To the best of our knowledge, this is the first reported case of gossypiboma in Sudan in 2024, highlighting diagnostic challenges and the need for preventive protocols. Root cause analysis of accidents, enhanced training, application of advanced technologies and a collaborative culture in the operating room can prevent the occurrence of such incidents. This case underscores the importance of meticulous surgical protocols and continuous improvement in safety measures to prevent retained surgical items, ensuring patient safety and optimal outcomes.

Background: A growing number of countries have reported sharp increases in the use and harm of opioid analgesics. High rates of new opioid initiation are observed in postoperative patients. In response, various tertiary care institutions have developed opioid exit plans (OEPs) to curb potential opioid-related harm.

Methods: PubMed and Embase were systematically searched to identify, summarize, and compare the interventional elements of OEPs for postoperative patient populations published from January 1, 2000, to June 4, 2024. Two researchers independently screened the articles for eligibility following the PRISMA 2020 guidelines, extracted the data, and assessed the study quality and risk of bias. Data synthesis was performed for study characteristics, intervention details, efficacy, and development.

Results: A total of 2,585 articles were screened, eight of which met the eligibility criteria. All studies were conducted in North America and focused on orthopedic surgery patients following total hip or knee arthroplasty (n = 5) or neurosurgery (n = 3). Most studies (n = 7) included a pre-post (n = 4) or randomized clinical design (n = 3). Three studies were of good quality, and none had a low risk of bias. The interventions varied and ranged from educational sessions (n = 1) to individualized tapering protocols (n = 4) or a combination of the two (n = 2). Key elements were instructions on how to anticipate patients' postoperative need for opioid analgesics and tapering strategies based on 24-h predischarge opioid consumption. Six studies included efficacy as an endpoint in their analysis, of which four assessed statistical significance, with all four identifying that the OEPs were successful in reducing postoperative opioid use.

Conclusion: Despite differences in design and implementation, the identified OEPs suggest that they are efficacious in reducing outpatient opioid consumption. They provide a robust estimate of postoperative analgesic requirements and a rationale for tapering duration and rate. However, more rigorous studies are needed to evaluate their real-world effectiveness.

Background: Retained surgical items (RSI) are preventable events that pose a significant risk to patient safety. Current strategies for preventing RSIs rely heavily on manual instrument counting methods, which are prone to human error. This study evaluates the feasibility and performance of a deep learning-based computer vision model for automated surgical tool detection and counting.

Methods: A novel dataset of 1,004 images containing 13,213 surgical tools across 11 categories was developed. The dataset was split into training, validation, and test sets at a 60:20:20 ratio. An artificial intelligence (AI) model was trained on the dataset, and the model's performance was evaluated using standard object detection metrics, including precision and recall. To simulate a real-world surgical setting, model performance was also evaluated in a dynamic surgical video of instruments being moved in real-time.

Results: The model demonstrated high precision (98.5%) and recall (99.9%) in distinguishing surgical tools from the background. It also exhibited excellent performance in differentiating between various surgical tools, with precision ranging from 94.0 to 100% and recall ranging from 97.1 to 100% across 11 tool categories. The model maintained strong performance on a subset of test images containing overlapping tools (precision range: 89.6-100%, and recall range 97.2-98.2%). In a real-time surgical video analysis, the model maintained a correct surgical tool count in all non-transition frames, with a median inference speed of 40.4 frames per second (interquartile range: 4.9).

Conclusion: This study demonstrates that using a deep learning-based computer vision model for automated surgical tool detection and counting is feasible. The model's high precision and real-time inference capabilities highlight its potential to serve as an AI safeguard to potentially improve patient safety and reduce manual burden on surgical staff. Further validation in clinical settings is warranted.

Background: Delays in surgery start times can lead to poor patient outcomes and considerable increases in healthcare expenditures. This is especially true in developing countries that often face systemic inefficiencies, such as a shortage of operating rooms and trained surgical personnel. With substantial effects on patient outcomes, healthcare efficiency, and resource allocation, identifying delays in first-case elective surgery is a crucial area of research.

Methods: A multicenter observational study was conducted at three comprehensive and specialized hospitals in the Amhara region of Ethiopia from May 1 to October 30, 2023. The primary aim of the study was to determine the occurrence of late first-case start times, defined as a patient being in the operating room at or after the hospital's incision time of 2:30 a.m. The secondary aim was to discover potential root causes of delayed first-case start times. All patients scheduled for elective surgery as the first case on the operating list throughout the study period were included in the study. Every emergency, day case, after-hours case, and canceled case was excluded.

Results: A total of 530 surgical patients were included during the study window from May 1 to October 1, 2023. Of these, 41.5% were general surgeries, 20.4% were gynecology and obstetrics surgeries, and 13.2% were orthopedic surgery procedures. Before the procedure started, nine (1.7%) of the participants had prolonged discussion with a member of the surgical team. Patients who arrived in the operating room waiting area at or after 2:30 a.m. were 2.5 times more likely to experience a first-case start time delay than those who arrived before or at 2:00 a.m. (AOR = 2.50; 95% CI: 1.13-5.14). Furthermore, participants with abnormal investigation results were 2.4 times more likely to have a late first-case start time (AOR = 2.41; 95% CI: 1.06, 5.50). Moreover, the odds of a late first-case start time were increased by 10.53 times with the surgeon being in the operating room at or after 2:30 a.m. (AOR = 10.53; 95% CI: 5.51, 20.11).

Conclusion: The research highlights a significant occurrence of delayed start times for the first elective surgical procedures. Therefore, directing attention to aspects such as ensuring patients and surgical teams arrive promptly (by or before 2:00 a.m.) and timely evaluation and communication of investigative findings before the scheduled surgery day could facilitate efforts to maximize operating room efficiency and enhance patient health outcomes.

Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Sammelstudie, 1986), the Major Trauma Outcome Study (MTOS) about survival, and the Trauma Audit and Research Network (TARN) pioneered multi-hospital data collection. Large trauma registries, like the German Trauma Registry (TR-DGU) helped improve evidence levels but were still constrained by predefined data sets and limited physiological parameters. The improvement in the understanding of pathophysiological reactions substantiated that decision making about fracture care led to development of patient's tailored dynamic approaches like the Safe Definitive Surgery algorithm. In the future, artificial intelligence (AI) may provide further steps by potentially transforming fracture recognition and/or outcome prediction. The evolution towards flexible decision making and AI-driven innovations may be of further help. The current manuscript summarizes the development of big data from local databases and subsequent trauma registries to AI-based algorithms, such as Parkland Trauma Mortality Index and the IBM Watson Pathway Explorer.

Background: Cervical spondylotic myelopathy (CSM) is a prevalent degenerative condition resulting from spinal cord compression and injury. Laminectomy with posterior spinal fusion (LPSF) is a commonly employed treatment approach for CSM patients. This study aimed to assess the effectiveness of machine learning models (MLMs) in predicting clinical outcomes in CSM patients undergoing LPSF.

Methods: A retrospective analysis was conducted on 329 CSM patients who underwent LPSF at our institution from Jul 2017 to Jul 2023. Neurological outcomes were evaluated using the modified Japanese Orthopaedic Association (mJOA) scale preoperatively and at the final follow-up. Patients were categorized into two groups based on clinical outcomes: the favorable group (recovery rates ≥ 52.8%) and the unfavorable group (recovery rates < 52.8%). Potential predictors for poor clinical outcomes were compared between the groups. Four MLMs-random forest (RF), logistic regression (LR), support vector machine (SVM), and k-nearest neighborhood (k-NN)-were utilized to predict clinical outcome. RF model was also employed to identify factors associated with poor clinical outcome.

Results: Out of the 329 patients, 185 were male (56.2%) and 144 were female (43.4%), with an average follow-up period of 17.86 ± 1.74 months. Among them, 267 patients (81.2%) had favorable clinical outcomes, while 62 patients (18.8%) did not achieve favorable results. Analysis using binary logistic regression indicated that age, preoperative mJOA scale, and symptom duration (p < 0.05) were independent predictors of unfavorable clinical outcomes. All models performed satisfactorily, with RF achieving the highest accuracy of 0.922. RF also displayed superior sensitivity and specificity (sensitivity = 0.851, specificity = 0.944). The Area under the Curve (AUC) values for RF, Logistic LR, SVM, and k-NN were 0.905, 0.827, 0.851, and 0.883, respectively. The RF model identified preoperative mJOA scale, age, symptom duration, and MRI signal changes as the most significant variables associated with poor clinical outcomes in descending order.

Conclusions: This study highlighted the effectiveness of machine learning models in predicting the clinical outcomes of CSM patients undergoing LPSF. These models have the potential to forecast clinical outcomes in this patient population, providing valuable prognostic insights for preoperative counseling and postoperative management.

The Cochrane systematic review and meta-analysis published in 2022 that compared videolaryngoscopy (VL) with direct laryngoscopy (DL) for facilitating tracheal intubation in adults found that all three types of VL device (Macintosh-style, hyper-angulated and channeled) reduced the risk of failed intubation and increased the likelihood of first-pass success. We report the findings of a subgroup re-analysis of the 2022 Cochrane meta-analysis data focusing on the Macintosh-style VL group. This was undertaken to establish whether sufficient evidence exists to guide airway managers in making purchasing decisions for their local institutions based upon individual device-specific performance. This re-analysis confirmed the superiority of Macintosh-style VL over Macintosh DL in elective surgical patients, with similar efficacy demonstrated between the Macintosh-style VL devices examined. Thus, when selecting which VL device(s) to purchase for their hospital, airway managers decisions are likely to remain focused upon issues such as financial costs, portability, cleaning schedules and previous device experience.