Prediction Of Surgical Outcome Research Articles

Role of respiratory secretion culture in the surgical outcome prediction of bacterial empyema.

Thoracic empyema is a serious infection. Video-assisted thoracoscopic surgery is a recommended treatment, and pleural fluid and tissue cultures are collected intraoperatively. The combination of a pleural peels tissue culture and a pleural fluid culture improves the positive culture rate. We aimed to investigate the role of respiratory secretion cultures to determine the optimal management for improving surgical outcome. The study analyzed 225 adult patients with phase II/III thoracic empyema who underwent thoracoscopic decortication. Respiratory secretion cultures were obtained and compared with pleural cultures. Key outcomes were culture positivity and pathogen consistency, with secondary outcomes including intensive care unit stay, hospital stay, and mortality. There were 225 empyema patients with either a positive pleural fluid culture or a positive pleural peel tissue culture. Of these, 76 patients had positive respiratory secretion culture findings during hospitalization. The most common pathogen species were Pseudomonas aeruginosa (44%) and Klebsiella pneumoniae (16%) in the respiratory secretion cultures and Streptococcus spp. (38%) and Klebsiella pneumoniae (12%) in the pleural cultures. There were 30 patients having a common pathogen in the respiratory secretion culture and in the pleural fluid/tissue culture. Poor outcome measures were found in these patients, including the longer use of antibiotics preoperatively [2.50 (1.00-6.00) days versus 5.00 (2.75-11.00) days, p = 0.006] and a higher mortality rate during hospitalization (40.0% versus 17.4%, p = 0.002). Respiratory secretion cultures are vital for predicting surgical outcomes in bacterial empyema, and prompt specimen collection can improve patient survival.

Are We Able to Optimize Outcomes and Predict Complications in Pelvic Floor Surgery With a Better Understanding of Hormonal, Microbial and Other Factors? A Report From the ICI-RS 2024.

Pelvic organ prolapse (POP) is a common condition, affecting women worldwide and is known to have a significant impact on Health Related Quality of Life (HRQoL). Although there are various treatment options available, including pelvic floor muscle training and support pessaries, many women opt for or require surgery, with a lifetime risk of needing surgery of 12%-19%. As with any operation, this does not come without its complications and the reoperation rate following POP surgery is up to 36%. This International Consultation on Incontinence-Research Society (ICI-RS) report aims to look at the different factors which may play a role in objective and subjective outcomes following pelvic floor surgery and to summarize the evidence and uncertainties regarding prediction of POP surgical outcomes, how to optimize them and the tools available to predict them. Research question proposals to further this field have been highlighted. At ICI-RS 2024, the evidence for predicting the outcomes from POP surgery and methods to optimize outcomes were discussed and presented in this paper. There are many reasons why POP surgery may fail, such as variations in lifestyle and occupation, persistent constipation, failure in the perineal body, connective tissue types or the shape of the pelvis. There may also be inherent conditions of the vagina, such as hormonal or microbial features. The literature lacks evidence about the potential use of advanced statistical modeling or supervised machine learning in the development of management plans for patients with POP. Furthermore, future research is needed to determine the role of UDS in the preoperative evaluation of POP patients. High-quality powered studies are required to assess optimization for long-term outcomes of pelvic surgery and then, once these are well established, and possible interventions are elucidated, prediction modeling can have a real impact clinically.

Prediction of surgical outcomes in ovarian cancer based on CT imaging

This study aims to investigate the commonly used prediction models for the outcomes of ovarian cancer surgery. The study will select 71 cases from the TCGA-OV dataset and divide them into a training set (n=51) and a validation set (n=20) at a ratio of 7:3. The project is based on PyRadiomics Python3.0.1 and extracts 107 basic image features from the image group data, including first-order statistics, shape, gray-level co-occurrence matrix, gray-level size zone matrix, GLRLM, etc. On this basis, the LASSO algorithm is used for feature dimensionality reduction. In building the model, a fuzzy matrix is used to analyze multiple evaluation indicators, including the area under the receiver operating characteristic (ROC) curve (AUC). This study hopes to provide more precise diagnostic and treatment plans for patients, with the goal of achieving better treatment outcomes.

A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptogenic network (EN). However, EN dynamics is highly variable across patients, hindering the development of diagnostic biomarkers. Without relying on specific connectivity variables, we focused on a general hypothesis that the EN undergoes the greatest magnitude of connectivity change during seizure generation, compared to other brain networks. To test this hypothesis, we developed a novel method for quantifying connectivity change between network states and applied it to identify surgical resection areas. A network state was represented by random snapshots of connectivity within a defined time interval of an intracranial EEG recording. A binary classifier was applied to classify two network states. The classifier generalization performance estimated by cross-validation was employed as a continuous measure of connectivity change. The algorithm generated a network by iteratively adding nodes until the connectivity change magnitude decreased. The resulting network was compared to the surgical resection, and the overlap score was used to predict post-surgical outcomes. The framework was evaluated in a consecutive cohort of 21 patients with a post-surgical follow-up of minimum 3 years. The best overlap between connectivity change networks and resections was obtained at the transition from pre-seizure to seizure (surgical outcome prediction ROC-AUC=90.3%). However, all patients except one were correctly classified when considering the most informative time intervals. Time intervals proportional to seizure length were more informative than the almost universally used fixed intervals. This study demonstrates that connectivity can be successfully classified with a machine learning analysis and provide information for distinguishing a separate epileptogenic functional network. In summary, the connectivity change analysis could accurately identify epileptogenic networks validated by surgery outcome classification. Connectivity change magnitude at seizure transition could potentially serve as an EN biomarker. The tool provided by this study may aid surgical decision-making.

Integration of cone beam computed tomography for interdisciplinary diagnosis and treatment planning

Cone-beam computed tomography (CBCT) has revolutionized dental imaging by providing precise three-dimensional visualization of hard tissue structures with minimal radiation exposure compared to conventional CT. This review explores the integration of CBCT in interdisciplinary diagnosis and treatment planning across various fields of dentistry. CBCT offers versatile applications including assessment of impacted teeth, airway analysis, temporomandibular joint evaluation, detection of jaw pathologies, and precise diagnosis of dento-maxillofacial fractures. Its ability to generate static and rotational 3D images aids in surgical planning and treatment outcome prediction. Furthermore, CBCT plays a crucial role in orthodontics for localization of impacted teeth and evaluation of craniofacial anomalies such as cleft lip and palate. In periodontics and implantology, CBCT facilitates detailed assessment of periodontal structures, bone defects, and precise implant placement. Additionally, CBCT assists in maxillofacial prosthetics by enabling virtual surgical planning and custom prosthetic fabrication. This paper underscores CBCT as an indispensable tool in modern dental practice, enhancing diagnostic accuracy, treatment precision, and patient outcomes across diverse clinical scenarios.

The 5-Item Modified Frailty Index (mFI-5) for risk stratification of patients with infective endocarditis undergoing cardiac surgery

Abstract Background population aging has led to an increased burden of frailty as an emerging global health issue, and variables for its quantification in patients with infective endocarditis (IE) are lacking in current surgical risks scores. The 5-item modified frailty index (mFI-5) score has shown to be effective in the prediction of surgical outcomes in other medical scenarios. Purpose this study aimed to assess the role of the mFI-5 in the prediction of surgical outcomes in patients with left-sided IE undergoing cardiac surgery. Methods a total of 112 cases of confirmed left-sided IE undergoing surgery, were consecutively collected and followed during one year after diagnosis in two tertiary hospitals between 2016 and 2021. The mFI-5 score included 5 baseline patient-specific comorbidities: diabetes, congestive heart failure, non-independent functional status (Barthel index <80), hypertension and chronic obstructive pulmonary disease (range 0-5), and patients were categorised as mFI-low (<2, n=65), and mFI-high (>=2, n=47). Results mean age was 66 [SD 15] years, 67% were men and community-acquired infections were 75%. As shown in Table 1, mFI-high patients were older and, aside from the specific comorbidities included in the score, with more chronic kidney disease. The most isolated pathogen was S. viridans (n=32), predominantly in mFI-low, in contrast to enterococcal and polymicrobial IEs, which were more frequent in mFI-high. Larger vegetations were reported in mFI-high [16 (10) vs 13 (9) mm, p 0.03], with no differences regarding infection site or periannular complications. Acute kidney injury, decompensated heart failure and higher natriuretic peptides were more common in mFI-high. Regarding mortality, in-hospital deaths were more frequent in mFI-high group. When analyzing mFI-score as a discrete variable, in-hospital mortality was associated with higher median scores values [2 (2) vs 1(1) points, p 0.02], being 2 points the optimal cutoff point for prediction [AUC 0.64 (95% CI 0.54-0.73), Fig.1A]. Among survivors who completed follow-up (n=82), rehospitalization and all-cause mortality rates in mFI-high exceeded those from mFI-low. Predictors of in-hospital and 1-year mortality were assessed by logistic regression (Fig. 1B), confirming the independent prognostic association of mFI-5-score in patients with IE undergoing surgery. Conclusions patients with IE undergoing cardiac surgery with higher mFI-5 scores were older, more predisposed to enterococcal and polymicrobial infections, and had a greater comorbidity burden with a more complicated clinical course. The mFI-score was independently associated with in-hospital mortality as well as rehospitalizations and 1-year mortality, positioning itself as an additional tool for risk stratification in this cluster of patients.Table 1Figure 1

Temporal Lobe Epilepsy with Bitemporal Interictal Epileptiform Discharges: Effects of Sleep and Wakefulness

Introduction. Independent bitemporal interictal discharges are often found in patients with temporal lobe epilepsy. The likelihood of registering epileptiform activity (EA) is higher during sleep. Assessment of bitemporal interictal epileptiform discharges (BIEDs) with various discharge predominance ratio is used for presurgical evaluation of epilepsy patients and prediction of surgical outcomes. Our objective was to determine the predominant side (PS) in patients with bitemporal epilepsy using the incidence of epileptiform discharges for each sleep stage. Materials and methods. We analyzed 45 recordings of 10–24 h long-term video-EEG monitoring (LTM) in patients with bitemporal EA. For each recording, the total incidence of EA (IEA) and EA incidence for wakefulness and for each sleep stage were calculated individually. We also assessed the discharge predominance index (DPI) as a ratio of IEA in the predominant and contralateral sides for the entire recording and for each sleep stage. Results. We observed an IEA increase with sleep deepening, with maximum values observed during N2 and N3 sleep stages. The minimum IEA values were recorded during REM sleep; nevertheless, most of the REM sleep discharges were detected on the PS. DPI values were the highest and the most stable during N2 and N3 stages. Conclusion. The findings of our study demonstrate an increase in DPI values with non-rapid eye movement (NREM) sleep deepening in patients with bitemporal localization of EA. Despite the protective effects of REM sleep (i.e., reducing the likelihood of EA), it may be pivotal in lateralization of EA in patients with BIEDs. The PS is generally determined by a higher DPI during N2 and N3 stages.

Recurrence and Risk Factors of Giant Cell Tumors in Hand Bones: A Systematic Review.

Giant cell tumor of bone (GCTB) is a locally aggressive tumor that may affect the bones of the hand and rarely causes pulmonary metastasis. It exhibits a variable recurrence rate after surgical interventions, which presents challenges in its management. This systematic review aims to delineate recurrence rates and identify risk factors for GCTB in the hand. We conducted a systematic literature search in April 2024, following PRISMA guidelines, on PubMed and TDNet for studies reporting postsurgical recurrence of GCTB in the hand. Cohort and case-control studies provided recurrence rates, whereas case reports and series were utilized to identify risk factors, compensating for the sparse data in the primary studies. We used descriptive statistics, χ2 tests, and logistic regression to analyze demographics, lesion characteristics, treatments, and outcomes. We reviewed 13 cohort and case-control studies involving 244 patients, finding an overall recurrence rate of 19.57%. Curettage was associated with higher recurrence rates compared with other surgical methods. After additional review of case reports, a limited range of motion in patients emerged as a significant protective factor against recurrence, suggesting potential benefits in surgical management and outcome prediction. The significant recurrence rate associated with curettage highlights the need for alternative surgical strategies in GCTB management of the hand. The protective role of limited ROM underscores the importance of thorough preoperative assessments to optimize surgical approaches and enhance patient outcomes.

A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery

The aim of this research was to design a Machine Learning (ML) approaches to predict surgical outcome associated with perioperative risks factors among patients undergoing elective surgery. The research employed descriptive cross-sectional survey and a sample size of 292 patients. Only adult patients undergoing elective surgery were considered. Machine Learning (ML) Algorithm such as Logistic regression, Support vector machine, k-nearest neighbors and random forest were used to provide insights into how different factors such as patient related perioperative risk, procedure related perioperative risk and health system related perioperative risk influence the likelihood of successful surgical outcome. The study found that Random Forest model achieved the highest cross validation accuracy of 100%, which means it correctly classified all data points in the test set. It implies that the random Forest model was the most suitable for classifying surgical outcome among elective surgery patient at Chuka County Referral Hospital. It had a Kappa of 1 indicating a perfect agreement between its predictions and the ground truth in comparison with other algorithms. In addition, Random Forest model achieves a perfect score (1.0) for sensitivity, precision, F1-Score, and balanced accuracy. This suggests that the model is doing extremely well at correctly classifying both positive and negative cases. Availability of main surgical supplies (health system related perioperative risk factors) had the highest score indicating that it was more important factor for the models predictions than other perioperative risk factors. In this study, the Machine Learning analysis identified unknown parameters associated with successful surgical outcome. An application of Machine Learning algorithms as a decision support tool could enable the medical health practitioners to predict the surgical outcome of patients undergoing elective surgery and consequently optimize and personalize clinical management of patient.

Revisiting Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (POSSUM) and Portsmouth-POSSUM (P-POSSUM) Scores: Are They Valid in Cases of Ileal Perforation?

Introduction Ileal perforation due to typhoid is common in tropical countries, and the ensuing secondary peritonitis is treated by resuscitation and surgery. The Physiological and Operative Severity Scorefor the Enumeration of Mortality and Morbidity (POSSUM) was developed to predict postoperative outcomes to overcome systemic obstacles in any healthcare setupand is considered fairly accurate. The Portsmouth-POSSUM (P-POSSUM) score was developed as a corrective scoring system for overestimations made by the POSSUM score vis-à-vis mortality. Our study aimed to examine the validity of these two scores in the postoperative prediction of surgical outcomes in patients with ileal perforation. Materials and methods An observational study involving 40 patients diagnosed with ileal perforations was undertaken over 18 months. The postoperative outcome for each patient was calculated as per the POSSUM and P-POSSUM parameters. Statistical analysis was done using SPSS(IBM Corp., Armonk, NY) and the results were tabulated. Results We found that age, gender, respiratory dysfunction, propensity for multiple surgeries, duration of surgery, co-morbidities, underlying malignancy, and systolic blood pressure played a significant role in determining postoperative outcomes. Haemoglobin, potassium, and urea levels were also found to be significantly associated with outcome. Cardiac signs, pulse, white blood cell count, Glasgow Coma Scale score, sodium, and electrocardiography, part of the physiological score parameters, were found to be insignificant in the prediction of postoperative outcomes. Among the intraoperative parameters, peritoneal soiling was found to be insignificant. Conclusion Some parameters inherent to POSSUM and P-POSSUM calculations appear to bear no statistical significance to the final score, highlighting that these need to be revisited and perhaps modified to further simplify the calculation.The POSSUM score is an excellent predictor of postoperative morbidity and mortality in ileal perforation patients but is of questionable reliability due to its tendency to overestimate them. P-POSSUM has a better predictive power of postoperative mortality by correcting POSSUM mortality overestimation.

Advancements in treatment strategies for periprosthetic joint infections: A comprehensive review

Periprosthetic joint infection (PJI) presents a critical challenge in orthopedic care, contributing to significant patient morbidity and healthcare costs. This burden is expected to increase secondary to growing demand for total joint arthroplasty (TJA). Despite the profound significance of PJI, there is currently no universally accepted “gold standard” diagnostic criteria using serum biomarker thresholds; latest criteria fail to differentiate acute infections from chronic or consider time since initial surgery. Furthermore, contemporary PJI treatment, which conventionally requires 2-stage revision surgery in conjunction with rigorous antibiotic treatment, can be particularly taxing on patients. Fortunately, recent years have seen marked evolution in both PJI diagnosis and treatment methods. Contemporary research supports time-dependent serum biomarker thresholds with greater sensitivity and specificity than previously reported, as well as alternative surgical options which may be more suitable for certain patients.The following narrative review aims to describe the significance and pathogenesis of PJI before characterizing current challenges, novel innovations, and the future landscape of PJI diagnosis and management. Here, we spotlight the emerging utility of novel biomarkers and metagenomic next-generation sequencing for diagnosis, advancements in patient-centered surgical outcome prediction tools for PJI risk assessment and prevention, and evolving surgical techniques including 1-stage and a “hybrid” 1.5-stage revision surgeries. Additionally, we explore cutting-edge therapeutic modalities including peptide and bacteriophage-based treatments, intraoperative anti-biofilm gel, the VT-X7 antibiotic pump, and promising immune-based interventions. Ultimately, these advancements hold the potential to revolutionize PJI management, offering hope for improved outcomes and reduced burdens on healthcare systems.

Urinary-based detection of MSL, HE4 and CA125 as an additional dimension for predictive and prognostic modelling in ovarian cancer.

We have recently described a predictive/prognostic model for ovarian cancer, exploiting commonly available clinico-pathological parameters and the ovarian serum biomarkers mesothelin (MSL), human epididymis protein 4 (HE4) and cancer-antigen 125 (CA125). Considering urine as a prototype non-invasive sample, we investigated whether serum levels of these biomarkers are mirrored in urine and compared their clinical relevance in matched serum vs. urine samples. MSL, HE4 and CA125 were quantified in urinary (n=172) and matched serum samples (n=188) from ovarian cancer patients (n=192) using the Lumipulse® G chemiluminescent enzyme immunoassay (Fujirebio). While absolute concentrations of MSL or CA125 were higher in serum than in matched urine samples, HE4 concentrations were considerably higher in urine than in serum. Nonetheless, the levels of all three biomarkers strongly correlated between matched serum vs. urine samples and were unrelated to BRCA1/2 mutational status. Consequently, prediction of surgical outcome or relapse/death by MSL, HE4 or CA125 was similarly efficient among urinary- vs. serum-based detection. HE4 provided the highest capacity to predict surgical outcome or relapse/death among both body fluids (urine: AUC=0.854; serum: AUC=0.750, respectively). All clinically relevant findings regarding the investigated urinary biomarkers were equally reproducible among raw vs. creatinine-normalized datasets, suggesting that normalization may have subordinate priority for urine-based analysis of these biomarkers. We report that the capacity of MSL, HE4 and CA125 to predict surgical outcome and relapse/death is equivalent between serum vs. urine-based detection. Urinary biomarkers, in particular HE4, may provide an additional dimension for prognostic modeling in ovarian cancer.

Levator palpebralis superioris histopathologic findings in congenital ptosis and surgical outcome after its resection: Is there any association?

To evaluate the histopathologic findings of Levator palpebralis superioris (LPS) muscle biopsy after LPS resection for treatment of congenital ptosis and its possible relation with surgical outcomes.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 4 Given name: [Seyed Mohsen] Last name [Rafizadeh]. Author 6 Given name: [Seyed Ali] Last name [Sonbolestan].Also, kindly confirm the details in the metadata are correct.The author names and the sequence are correct. Congenital ptosis patients were enrolled in this retrospective study. All of them underwent full ophthalmologic examination included of Margin-reflex distance 1 (MRD-1) and LPS function measurement preoperatively. The patients were followed for 3months for the postoperative period and after that the measurements were repeated. Histologic parameters including percentages of fat, striated and smooth muscle, and fibrous tissue. The histopathologic findings and their possible correlation with the measurements are analyzed. Sixty-seven patients with unilateral congenital ptosis were enrolled. 45 patients (67.2%) were males. The mean age of patients was 16.10 ± 11.18years. The patients' MRD-1 was improved significantly from 0.82 ± 1.26mm to 3.85 ± 1.25mm after LPS resection (P = 0.000). The success rate was 80.3%. There were no correlations between MRD change and histopathologic tissue percentages but significant correlation was found between success of surgery and fibrous tissue percentage of resected sample (P = 0.033). The histopathology of the LPS may be useful in prediction of surgical outcome after LPS resection in congenital ptosis patients. The percentage of fibrous tissue play an important role.

EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes.

Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties. We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time. Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes. Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.

Urinary-based detection of MSL, HE4, and CA125 as an additional dimension for predictive and prognostic modelling in ovarian cancer.

e17567 Background: We have recently described a predictive/prognostic model for ovarian cancer, exploiting commonly available clinico-pathological parameters and the ovarian serum biomarkers mesothelin (MSL), human epididymis protein 4 (HE4) and cancer-antigen 125 (CA125). Considering urine as a prototype non-invasive sample, we investigated whether serum levels of these biomarkers are mirrored in urine and compared their clinical relevance in matched serum vs. urine samples. Methods: MSL, HE4 and CA125 were quantified by Lumipulse G chemiluminescent enzyme immunoassay (Fujirebio). In total, this study was based on 1080 biomarker detection runs from 188 serum and 172 urine samples from 192 ovarian cancer patients. Results: While absolute concentrations of MSL and CA125 were higher in serum than in urine, serum HE4 concentrations were considerably lower than urinary HE4 concentrations. Nonetheless, relative intra-patient levels of all three biomarkers strongly correlated between matched serum vs. urinary samples and were unrelated to BRCA1/2 mutational status. Consequently, prediction of surgical outcome or relapse/death by MSL, HE4 or CA125 was similarly efficient among urinary- vs. serum-based detection. HE4 provided the highest capacity in predicting surgical outcome and relapse/death among both body fluid types (urinary HE4: AUC=0.854; AUC=0.750, respectively). All clinically relevant findings of urinary MSL, HE4 and CA125 were reproducible among raw vs. creatinine-normalized datasets, suggesting that normalization may have subordinate priority for urine-based analysis. Conclusions: We report that the capacity of MSL, HE4 and CA125 for predicting surgical outcome and relapse/death is equivalent between serum vs. urine-based detection. Urinary biomarkers, in particular HE4, may provide an additional dimension for prognostic modeling in ovarian cancer.

Development and validation of a machine learning-based postoperative prognostic model for plasma cell neoplasia with spinal lesions as initial clinical manifestations: a single-center cohort study.

Spinal multiple myeloma (MM) and solitary plasmacytoma of bone (SPB), both plasma cell neoplasms, greatly affect patients' quality of life due to spinal involvement. Accurate prediction of surgical outcomes is crucial for personalized patient care, but systematic treatment guidelines and predictive models are lacking. This study aimed to develop and validate a machine learning (ML)-based model to predict postoperative outcomes and identify prognostic factors for patients with spinal MM and SPB. A retrospective analysis was conducted on patients diagnosed with MM or SPB from 2011 to 2015, followed by prospective data collection from 2016 to 2017. Patient demographics, tumor characteristics, clinical treatments, and laboratory results were analyzed as input features. Four types of ML algorithms were employed for model development. The performance was assessed using discrimination and calibration measures, and the Shapley Additive exPlanations (SHAP) method was applied for model interpretation. A total of 169 patients were included, with 119 for model training and 50 for validation. The Gaussian Naïve Bayes (GNB) model exhibited superior predictive accuracy and stability. Prospective validation on the 50 patients revealed an area under the curve (AUC) of 0.863, effectively distinguishing between 5-year survivors and non-survivors. Key prognostic factors identified included International Staging System (ISS) stage, Durie-Salmon (DS) stage, targeted therapy, and age. The GNB model has the best performance and high reliability in predicting postoperative outcomes. Variables such as ISS stage and DS stage were significant in influencing patient prognosis. This study enhances the ability to identify patients at risk of poor outcomes, thereby aiding clinical decision-making.

High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker

Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables.

Impact of preoperative factors and waiting time on post-appendectomy complications: a retrospective study

BackgroundSeveral factors are associated with increased postoperative complications after appendectomies. However, few studies combined these potential factors for comprehensive prediction of surgical outcomes. Whether high-risk patients benefit from a shorter waiting time for surgery remains unclear. This study aimed to explore the impact of surgical waiting time and potential risk factors on postoperative complications.MethodsA total of 1343 patients diagnosed with acute appendicitis requiring an emergent appendectomy were included from 2013 to 2018. The preoperative risk factors associated with postoperative complications were selected and the probability of postoperative complications was calculated by multivariate logistic regression model. Patients were divided into four groups based on the risk (high & low) and time to surgery (> 12 & ≤12 hours). The odds ratios for complications were evaluated between groups.ResultsThe selected risk factors included age, neutrophil-lymphocyte ratio, systemic inflammatory response syndrome and abdominal pain duration. Compared with low-risk patients with time to surgery ≤12 hours, high-risk patients with time to surgery > 12 hours had significant increased overall postoperative complication rate (16.85% vs. 8.16%, p = 0.002) and a trend toward increased surgical site infection rate (10.99% vs. 6.46%, p = 0.058). When operated within 12 hours, there was no difference in outcomes between high- and low-risk patients. On the other hand, time to surgery > 12 hours did not increase complication rate in low-risk patients.ConclusionsThe surgical outcome may be affected by preoperative factors and time to surgery. It is suggested that high-risk patients receive appendectomy within 12 hours to avoid increased postoperative complications.

Presurgical management trends and nasoalveolar molding usage for infants with cleft lip and palate in the capital of a developing country.

Nasoalveolar molding (NAM) application is among presurgical management (PSM) techniques used for infants with cleft lip and palate (CLP). It helps to approximate the palatal cleft and to reshape the nasoalveolar complex prior to primary lip repair. This study aimed to explore types of PSM and the dental speciality provision for infants with CLP in Baghdad. The status of NAM usage and surgeons' perceptions toward NAM usage were assessed. This is a cross-sectional paper-based questionnaire study that collected responses of surgeons perform primary lip and nose repair regarding PSM. The questionnaire was distributed amongst public and private hospitals in Baghdad. Twenty surgeons were enrolled (only those surgeons who perform primary repair for infants with CLP); two females and eighteen males. The majority of participants' responses suggested that the majority of infants with CLP were provided with baby feeding plates and lip straps. Six surgeons reported that a percentage of their patients who have been provided with NAM. PSM in Baghdad was mostly supplied by orthodontists and plastic surgeons, and the next most likely providers were prosthodontists. 82.35% of the surgeons found that primary surgical repair procedures were easier with NAM than for the other infants. The rest have not perceived any differences. Orthodontists, surgeons and prosthodontists were involved in providing PSM. Baby feeding plates and lip straps were the most common PSM in Baghdad, although NAM is not uncommon. Most surgeons believe that using NAM made surgical procedures easier and permitted the prediction of surgical outcomes.

Prediction of Surgical Outcomes in Normal Pressure Hydrocephalus by MR Elastography.

Normal pressure hydrocephalus is a treatable cause of dementia associated with distinct mechanical property signatures in the brain as measured by MR elastography. In this study, we tested the hypothesis that specific anatomic features of normal pressure hydrocephalus are associated with unique mechanical property alterations. Then, we tested the hypothesis that summary measures of these mechanical signatures can be used to predict clinical outcomes. MR elastography and structural imaging were performed in 128 patients with suspected normal pressure hydrocephalus and 44 control participants. Patients were categorized into 4 subgroups based on their anatomic features. Surgery outcome was acquired for 68 patients. Voxelwise modeling was performed to detect regions with significantly different mechanical properties between each group. Mechanical signatures were summarized using pattern analysis and were used as features to train classification models and predict shunt outcomes for 2 sets of feature spaces: a limited 2D feature space that included the most common features found in normal pressure hydrocephalus and an expanded 20-dimensional (20D) feature space that included features from all 4 morphologic subgroups. Both the 2D and 20D classifiers performed significantly better than chance for predicting clinical outcomes with estimated areas under the receiver operating characteristic curve of 0.66 and 0.77, respectively (P < .05, permutation test). The 20D classifier significantly improved the diagnostic OR and positive predictive value compared with the 2D classifier (P < .05, permutation test). MR elastography provides further insight into mechanical alterations in the normal pressure hydrocephalus brain and is a promising, noninvasive method for predicting surgical outcomes in patients with normal pressure hydrocephalus.