From Shunt to Recovery: A Multidisciplinary Approach to Hydrocephalus Treatment in Severe Acquired Brain Injury Rehabilitation

Background: Hydrocephalus among Severe Acquired Brain Injury (SABI) patients remains overlooked during rehabilitation. Methods: A retrospective cohort study was carried out of traumatic and non-traumatic SABI patients with hydrocephalus, consecutively admitted over 9 years in a tertiary referral specialized rehabilitation hospital. Patients were treated with ventriculoperitoneal shunt before or during inpatient rehabilitation and assessed using the Level of Cognitive Functioning Scale and Disability Rating Scale. Logistic regression models were used to identify predictors of post-surgical complications. Linear regression models were used to investigate predictors of hospital length of stay (LOS), disability, and cognitive function. Results: Of the 82 patients, 15 had post-surgical complications and 16 underwent cranioplasty. Shunt placement complication risk was higher when fixed vs. when programmable pressure valves were used. A total of 56.3% achieved functional improvement at discharge and 88.7% improved in cognitive function; of the 82 patients, 56% were discharged home. In multiple regression analyses, higher disability at discharge was related to cranioplasty and longer LOS, while poorer cognitive function was associated with cranioplasty. Increase in LOS was associated with increasing time to shunt and decreasing age. Conclusions: A significant improvement in cognitive and functional outcomes can be achieved. Cranioplasty increased LOS, and fixed pressure valves were related to poorer outcomes.

A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank

Background: Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our understanding of the role of genetics in pregnancy-related disease complications. Methods: We built a disease–disease network (DDN) using UK Biobank (UKBB) summary data from a phenome-wide association study (PheWAS) to elaborate multiple disease associations. We also constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. We then applied graph-based semi-supervised learning (GSSL) to translate the connections in the egocentric DDNs to pathologic knowledge. Results: A total of 26 egocentric DDNs were constructed for each pregnancy-related phenotype in the UKBB. Applying GSSL to each DDN, we obtained complication risk scores for additional phenotypes given the pregnancy-related disease of interest. Predictions were validated using co-occurrences derived from UKBB electronic health records. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of 1.35 from 55.0% to 74.4% compared to the use of the full DDN. Conclusion: Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.

Assessing Risk of Severe Complications after Endoscopic Transnasal Transsphenoidal Surgery: A Comparison of Frailty, American Society of Anesthesiologists, and Comorbidity Scores

Objective This study aimed to improve age-independent risk stratification for patients undergoing endoscopic transnasal transsphenoidal (TNTS) approach to pituitary mass resection by investigating the associations between frailty, American Society of Anesthesiologists (ASA), and comorbidity scores with severe complications following TNTS. Design This study is a retrospective review. Setting This review was conducted utilizing the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Participants A total of 680 cases of TNTS identified from 2010 to 2013 were included in this study. Main Outcome Measures The modified frailty index (mFI) was calculated to quantify frailty. ASA and Charlson's comorbidity index (CCI) scores were obtained as physiologic status and comorbidity-based prognostic markers. Severe complications were separated into intensive care unit (ICU)-level complications, defined by Clavien–Dindo grade IV (CDIV) criteria, and mortality. Results Overall, 24 CDIV complications (3.5%) and 6 deaths (0.9%) were recorded. Scores for mFI (p = 0.01, R 2 = 0.97) and ASA (p = 0.04., R 2 = 0.87) were significantly correlated with CDIV complications. ASA scores were significantly correlated with mortality (p = 0.03, R 2 = 0.87), as well as independently associated with CDIV complication by multivariable regression models (odds ratio [OR] = 2.96, 95% confidence interval [CI]: 1.35–6.83, p < 0.01), while mFI was not. CCI was not significantly associated with CDIV complications or mortality. A multivariable regression model incorporating ASA had a lower Akaike's Information Criteria (AIC; 188.55) than a model incorporating mFI (195.99). Conclusion Frailty and physiologic status, as measured by mFI and ASA scores respectively, both correlate with ICU-level complications after TNTS. ASA scores demonstrate greater clinical utility than mFI scores; however, as they are more easily generated, uniquely correlated with mortality and independently associated with ICU-level complication risk on multivariable regression analysis.

Carotid artery direct access for mechanical thrombectomy: the Carotid Artery Puncture Evaluation (CARE) study

BackgroundIn acute ischemic stroke due to anterior large vessel occlusion (AIS-LVO), accessing the target occluded vessel for mechanical thrombectomy (MT) is sometimes impossible through the femoral approach. We aimed to evaluate the safety and efficacy of direct carotid artery puncture (DCP) for MT in patients with failed alternative vascular access.MethodsWe retrospectively analyzed data from 45 stroke centers in France, Switzerland and Germany through two research networks from January 2015 to July 2019. We collected physician-centered data on DCP practices and baseline characteristics, procedural variables and clinical outcome after DCP. Uni- and multivariable models were conducted to assess risk factors for complications.ResultsFrom January 2015 to July 2019, 28 149 MT were performed, of which 108 (0.39%) resulted in DCP due to unsuccessful vascular access. After DCP, 77 patients (71.3%) had successful reperfusion (modified Thrombolysis In Cerebral Infarction (mTICI) score ≥2b) and 28 (25.9%) were independent (modified Rankin Scale (mRS) score 0–2) at 3 months. 20 complications (18.5%) attributed to DCP occurred, all of them during or within 1 hour of the procedure. Complications led to extension of the intubation time in the intensive care unit in 7 patients (6.4%) and resulted in death in 3 (2.8%). The absence of use of a hemostatic closure device was associated with a higher complication risk (OR 3.04, 95% CI 1.03 to 8.97; p=0043).ConclusionIn this large multicentric study, DCP was scantly performed for vascular access to perform MT (0.39%) in patients with AIS-LVO and had a high rate of complications (18.5%). Our results provide arguments for not closing the cervical access by manual compression after MT.

Machine learning approaches for the prediction of postoperative complication risk in liver resection patients

Background For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions. Objective The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications. Methods The study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications. Results Random Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77–1), with an accuracy of 92.45% (95% CI 85–100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient’s BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients. Conclusions To reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized.

Classification Tree to Analyze Factors Connected with Post Operative Complications of Cataract Surgery in a Teaching Hospital

Background: Artificial intelligence (AI) is becoming ever more frequently applied in medicine and, consequently, also in ophthalmology to improve both the quality of work for physicians and the quality of care for patients. The aim of this study is to use AI, in particular classification tree, for the evaluation of both ocular and systemic features involved in the onset of complications due to cataract surgery in a teaching hospital. Methods: The charts of 1392 eyes of 1392 patients, with a mean age of 71.3 ± 8.2 years old, were reviewed to collect the ocular and systemic data before, during and after cataract surgery, including post-operative complications. All these data were processed by a classification tree algorithm, producing more than 260 million simulations, aiming to develop a predictive model. Results: Postoperative complications were observed in 168 patients. According to the AI analysis, the pre-operative characteristics involved in the insurgence of complications were: ocular comorbidities, lower visual acuity, higher astigmatism and intra-operative complications. Conclusions: Artificial intelligence application may be an interesting tool in the physician’s hands to develop customized algorithms that can, in advance, define the post-operative complication risk. This may help in improving both the quality and the outcomes of the surgery as well as in preventing patient dissatisfaction.

Imaging versus Intervention in Managing Small Unruptured Intracranial Aneurysms: A Cost-Effectiveness Analysis

<b><i>Objective:</i></b> Current guidelines recommend active surveillance with serial magnetic resonance angiography (MRA) for management of small, asymptomatic unruptured anterior circulation aneurysms (UIAs). We sought to determine the cost-effectiveness of active surveillance compared to immediate surgery. <b><i>Methods:</i></b> We developed a Markov cost-effectiveness model simulating patients with small (&#x3c;7 mm) UIAs managed by active surveillance via MRA, immediate surgery, or watchful waiting. Inputs for the model were abstracted from the literature and used to construct a comprehensive model following persons from diagnosis to death. Outcomes were quality-adjusted life-years (QALYs), lifetime medical costs (2015 USD), and incremental cost-effectiveness ratios (ICERs). Cost-effectiveness, deterministic, and probabilistic sensitivity analyses were performed. <b><i>Results:</i></b> Immediate surgical treatment was the most cost-effective management strategy for small UIAs with ICER of USD 45,772 relative to active surveillance. Sensitivity analysis demonstrated immediate surgery was the preferred strategy, if rupture rate was &#x3e;0.1%/year and if the diagnosis age was &#x3c;70 years, while active surveillance was preferred if surgical complication risk was &#x3e;11%. Probabilistic sensitivity analysis demonstrated that at a willingness-to-pay of USD 100,000/QALY, immediate surgical treatment was the most cost-effective strategy in 64% of iterations. <b><i>Conclusion:</i></b> Immediate surgical treatment is a cost-effective strategy for initial management of small UIAs in patients &#x3c;70 years of age. While more costly than MRA, surgical treatment increased QALY. The cost-effectiveness of immediate surgery is highly sensitive to diagnosis age, rupture rate, and surgical complication risk. Though there are a wide range of rupture rates and complications associated with treatment, this analysis supports the treatment of small, unruptured anterior circulation intracranial aneurysms in patients &#x3c;70 years of age.

Literature Review The Relationship Of Antenatal Class Towards Mothers’ Knowledge Of DangerousSign During Pregnancy

IN 2013, the World Health Organization, released data in the form of Maternal Mortality Rate (MMR) worldwide, and the number reached 289,000 per 100, 000 live births, which 99% of cases occurred in developing countries. Research aims to discover the relationship of antenatal class towards mothers’ knowledge of the dangerous sign during pregnancy. The result showed that there is a relationship of antenatal class towards mothers’ knowledge of dangerous sign during pregnancy, From this result, the researcher concludes that antenatal class could increase mothers’ knowledge of dangerous sign during pregnancy and may decrease the complication risk during the childbirth.

Patient safety during joint replacement surgery: experiences of operating room nurses

BackgroundAvoidable complications for surgical patients still occur despite efforts to improve patient safety processes in operating rooms. Analysis of experiences of operating room nurses can contribute to better understanding of perioperative processes and flow, and why avoidable complications still occur.AimTo explore aspects of patient safety practice during joint replacement surgery through assessment of operating room nurse experiences.MethodA qualitative design using semistructured interviews with 21 operating room nurses currently involved in joint replacement surgery in Sweden. Inductive qualitative content analysis was used.ResultsThe operating room nurses described experiences with patient safety hazards on an organisational, team and individual level. Uncertainties concerning a reliable plan for the procedure and functional reporting, as well as documentation practices, were identified as important. Teamwork and collaboration were described as crucial at the team level, including being respected as valuable, having shared goals and common expectations. On the individual level, professional knowledge, skills and experience were needed to make corrective steps.ConclusionThe conditions to support patient safety, or limit complication risk, during joint replacement surgery continue to be at times inconsistent, and require steady performance attention. Operating room nurses make adjustments to help solve problems as they arise, where there are obvious risks for patient complications. The organisational patient safety management process still seems to allow deviation from established practice standards at times, and relies on individual-based corrective measures at the ‘bedside’ at times for good results.

Incorporating Patient Preferences into a Decision-Making Model of Hand Trauma Reconstruction

Background: Few studies have addressed patient preferences in emergent surgical decision making. Aim of the study: Analyzing patient preferences for hand trauma reconstruction to propose a decision-making model. Methods: A conjoint analysis survey was developed with Sawtooth Software. Three common flaps—i.e., a cross-finger flap (CFF), a dorsal metacarpal artery perforator flap (DMAPF), and an arterialized venous flap (AVF)—were listed as treatment alternatives. Five attributes corresponding to these flaps were recovery time, total procedure, postoperative care methods, postoperative scar condition, and complication rate. Utility and importance scores were generated from the software, and preference characteristics were evaluated using cluster analysis. Results: The survey was completed by 197 participants with hand trauma. Complication risk received the highest importance score (42.87%), followed by scar condition (21.55%). Cluster analysis classified the participants as “conservative,” “practical,” and “dual-concern.”. The dual-concern and conservative groups had more foreign laborers and highly educated participants, respectively, than the other groups. Most participants in the conservative and practical groups preferred DMAPF, whereas those in the dual-concern group favored CFF. Our proposed model consisted of shared decision-making and treatment recommendation pathways. Conclusion: Incorporating patient preferences into the decision-making model can strengthen patient-centered care. Further research on the applications of the proposed model is warranted.