A simple scoring rule to predict survival to discharge after out of hospital cardiac arrest at the time of ED arrival.

BACKGROUND: It is important to be able to predict the chance of survival to hospital discharge upon ED arrival in order to determine whether to continue or terminate resuscitation efforts after out of hospital cardiac arrest. This study was conducted to develop and validate a simple scoring rule that could predict survival to hospital discharge at the time of ED arrival. METHODS: This was a multicenter retrospective cohort study based on a nationwide registry (Korean Cardiac Arrest Research Consortium) of out of hospital cardiac arrest (OHCA). The study included adult OHCA patients older than 18 years old, who visited one of 33 tertiary hospitals in South Korea from September 1st, 2015 to June 30th, 2020. Among 12,321 screened, 5471 patients were deemed suitable for analysis after exclusion. Pre-hospital ROSC, pre-hospital witness, shockable rhythm, initial pH, and age were selected as the independent variables. The dependent variable was set to be the survival to hospital discharge. Multivariable logistic regression (LR) was performed, and the beta-coefficients were rounded to the nearest integer to formulate the scoring rule. Several machine learning algorithms including the random forest classifier (RF), support vector machine (SVM), and K-nearest neighbor classifier (K-NN) were also trained via 5-fold cross-validation over a pre-specified grid, and validated on the test data. The prediction performances and the calibration curves of each model were obtained. Pre-processing of the registry was done using R, model training & optimization using Python. RESULTS: A total of 5471 patients were included in the analysis. The AUROC of the scoring rule over the test data was 0.7620 (0.7311-0.7929). The AUROCs of the machine learning classifiers (LR, SVM, k-NN, RF) were 0.8126 (0.7748-0.8505), 0.7920 (0.7512-0.8329), 0.6783 (0.6236-0.7329), and 0.7879 (0.7465-0.8294), respectively. CONCLUSION: A simple scoring rule consisting of five, binary variables could aid in the prediction of the survival to hospital discharge at the time of ED arrival, showing comparable results to conventional machine learning classifiers.

Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models.

BACKGROUND: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. METHODS: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. RESULTS: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153 were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI], 0.9352-0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612-0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860-1.0000); sensitivity, 0.9594 (95% CI, 0.9245-0.9943); specificity, 0.9714 (95% CI, 0.9162-1.0000); PPV, 0.9916 (95% CI, 0.9752-1.0000); NPV, 0.8718 (95% CI, 0.7669-0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825-0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845-0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087-0.9867); sensitivity, 0.9595 (95% CI, 0.9145-1.0000); specificity, 0.6500 (95% CI, 0.5022-0.7978); PPV, 0.8353 (95% CI, 0.7564-0.9142); NPV, 0.8966 (95% CI, 0.7857-1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. CONCLUSION: We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Common-path diffraction optical tomography for investigation of three-dimensional structures and dynamics of biological cells: erratum.

An erratum is presented to correct a typographical error on Fig. 1 in [Opt. Express 22(9), 10398 (2014)].

Common-path diffraction optical tomography for investigation of three-dimensional structures and dynamics of biological cells.

We present an optical holographic micro-tomographic technique for imaging both the three-dimensional structures and dynamics of biological cells. Optical light field images of a sample, illuminated by a plane wave with various illumination angles, are measured in a common-path interferometry, and thus both the three-dimensional refractive index tomogram and two-dimensional dynamics of live biological cells are measured with extremely high sensitivity. The applicability of the technique is demonstrated through quantitative and measurements of morphological, chemical, and mechanical parameters at the individual cell level.

Postoperative Longitudinal Outcomes in Patients with Residual Disc Fragments after Percutaneous Endoscopic Lumbar Discectomy.

BACKGROUND: Residual disc fragments are observed on immediate postoperative magnetic resonance imaging (MRI) in 2.8-15% of patients after percutaneous endoscopic lumbar discectomy (PELD). Considering the known postoperative longitudinal outcomes in patients with residual disc tissue, a 'watchful waiting' strategy may be preferable to immediate re-operation in patients with asymptomatic residual disc material. OBJECTIVES: The aim of the present study was to compare the longitudinal clinical outcomes between PELD patients in whom the complete removal of disc fragments was achieved (complete group) and those in whom residual disc fragments were observed on postoperative MRI (residual group). STUDY DESIGN: Retrospective nested case-control study. METHODS: A total of 225 patients were included (complete group, n=187 and residual group, n=38). Clinical assessments were performed using the visual analog pain score for the leg (VAS-L, x/10) and back (VAS-B, x/10) and the Korean version of the Oswestry Disability Index (K-ODI, x/45). A linear mixed-effects model was used to analyze changes during the first 24 postoperative months. RESULTS: One month after surgery, significant improvements in the VAS-L, VAS-B and K-ODI values were observed and were maintained during the first 24 postoperative months. No differences in these changes were noted between the groups. Early re-operation (during the first 3 postoperative months) was performed in 3 patients in the residual group (7.9%) and in 4 patients in the complete group (2.1%) (P = 0.10). LIMITATIONS: First, the study design was retrospective. Moreover, the number of patients was relatively small and therefore insufficient to achieve robust statistical power. Second, we did not explore the radiological outcomes in patients with asymptomatic residual disc material because follow-up MRI was only obtained to document symptom recurrence. CONCLUSION: When residual disc tissue is observed in asymptomatic patients, a 'watchful waiting' strategy may be preferable to immediate re-operation. However, an increased early re-operation rate is expected for patients with residual disc tissue. KEY WORDS: Discectomy, endoscopes, longitudinal studies, patient-reported outcome, percutaneous, reoperation, spine, residual disc.

Quantity of Disc Removal and Radiological Outcomes of Percutaneous Endoscopic Lumbar Discectomy.

BACKGROUND: Herniated intervertebral disc disease (HIVD) is a common cause of lower back and leg pain. Percutaneous endoscopic lumbar discectomy (PELD) is indicated when non-surgical treatments such as medication and interventions are intractable. Ruptured discs and loose fragments inside discs are removed during PELD. Nerve root decompression is usually assessed by visualizing the free movement of the traversing nerve root or epidural fat, the free passage of a probe into the epidural space, the depression of the annulus, and the removal of the expected ruptured discs and loose fragments based on preoperative magnetic resonance images (MRI). However, these criteria are subjective, and the quantity of the disc removal necessary for successful outcomes after PELD has not been investigated. OBJECTIVES: The present study investigates the amount of discectomy of PELD and its clinical and radiological outcomes. STUDY DESIGN: A retrospective case study (IRB Number H-1611-015-803). SETTING: University Medical Center, Seoul, Korea. METHODS: PELD was performed in 109 consecutive patients (M:F = 53:56; mean age, 37.4 years) using the transforaminal or interlaminar route. Ruptured disc fragments were first removed in all patients, and the graspable loose fragments under the disc were removed. After surgery, all removed disc fragments were placed into disposable syringes and manually compressed to measure their volume. The volume of herniated disc outside the disc boundary was calculated in MRI. The measured and calculated disc volumes were retrospectively compared. Clinical success was defined as an improvement in both the Oswestry Disability Index (ODI) and leg pain, as well as no recurrent symptoms. Radiological success was defined as the disappearance of herniated disc material outside the disc boundary based on postoperative MRI taken within one day after surgery. The follow-up period was 7.2 ± 5.2 months. RESULTS: Successful clinical outcomes were obtained in 96/109 (88.1%) patients in a median time of 3 months. Re-operation was performed in 3 patients due to recurrent discs in 2 patients and a residual disc in one patient. Predictors of clinical success were not identified, and the quantity of the removed disc was not associated with the clinical outcome. Radiological success was achieved in 93/109 (85.3%) patients. Of 13 patients with radiological failure, 2 patients showed clinical failure. A logistic regression analysis showed that the relative volume of the removed disc (%) compared with the volume of preoperative herniated disc based on the MRI was the only significant predictor (P < 0.001; OR = 0.96). When 100% of the calculated disc amount was removed during the operation, the probability of residual disc was 30%. When 131% of the calculated disc amount was removed, the probability of residual disc was 10%. LIMITATIONS: This study employed a retrospective design, and its inherent selection bias and limited statistical power should be considered. CONCLUSIONS: The amount of disc removal during PELD was not a significant predictor of clinical success after the primary ruptured fragments were removed. The relative volume of the removed disc based on the preoperative MRI might predict the postoperative MRI. KEY WORDS: Disc, lumbar vertebra, discectomy, surgery, endoscopy, volume.