Application of machine learning for lung cancer survival prognostication-A systematic review and meta-analysis.

Introduction: Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A comprehensive search was conducted in Medline, Embase, and Cochrane databases using a predefined search query. Two independent reviewers screened abstracts and conflicts were resolved by a third reviewer. Inclusion and exclusion criteria were applied to select eligible studies. Risk of bias assessment was performed using predefined criteria. Data extraction was conducted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. Meta-analytic analysis was performed to compare the discriminative ability of ML and LR models. Results: The literature search resulted in 3,635 studies, and 12 studies with a total of 211,068 patients were included in the analysis. Six studies reported confidence intervals and were included in the meta-analysis. The performance of ML models varied across studies, with C-statistics ranging from 0.60 to 0.85. The pooled analysis showed that ML models had higher discriminative ability compared to LR models, with a weighted average C-statistic of 0.78 for ML models compared to 0.70 for LR models. Conclusion: Machine learning models show promise in predicting overall survival in patients with lung cancer, with superior discriminative ability compared to logistic regression models. However, further validation and standardization of ML models are needed before their widespread implementation in clinical practice. Future research should focus on addressing the limitations of the current literature, such as potential bias and heterogeneity among studies, to improve the accuracy and generalizability of ML models for predicting outcomes in patients with lung cancer. Further research and development of ML models in this field may lead to improved patient outcomes and personalized treatment strategies.

Contributing Factors to Operating Room Delays Identified from an Electronic Health Record: A Retrospective Study.

The operating room (OR) is considered a major cost center and revenue generator for hospitals. Multiple factors contribute to OR delays and impact patient safety, patient satisfaction scores, and hospital financial performance. Reducing OR delays allows better utilization of OR resources and staffing and improves patient satisfaction while decreasing operating costs. Accurate scheduling can be the basis to achieve these goals. The objective of this initial study was to identify factors not normally documented in the electronic health record (EHR) that may contribute to or be indicators of OR delays. Materials and Methods. A retrospective data analysis was performed analyzing 67,812 OR cases from 12 surgical specialties at a small university medical center from 2010 through the first quarter of 2017. Data from the hospital's EHR were exported and subjected to statistical analysis using Statistical Analysis System (SAS) software (SAS Institute, Cary, NC). Results. Statistical analysis of the extracted EHR data revealed factors that were associated with OR delays including, surgical specialty, preoperative assessment testing, patient body mass index, American Society of Anesthesiologists (ASA) physical status classification, daily procedure count, and calendar year. Conclusions. Delays hurt OR efficiency on many levels. Identifying those factors may reduce delays and better accommodate the needs of surgeons, staff, and patients thereby leading to improved patient's outcomes and patient satisfaction. Reducing delays can decrease operating costs and improve the financial position of the operating theater as well as that of the hospital. Anesthesiology teams can play a key role in identifying factors that cause delays and implementing mitigating efficiencies.

Machine learning in medicine: It has arrived, let's embrace it.

Machine learning and artificial intelligence (AI) have arrived in medicine and the healthcare community is experiencing significant growth in their adoption across numerous patient care settings. There are countless applications for machine learning and AI in medicine ranging from patient outcome prediction, to clinical decision support, to predicting future patient therapeutic setpoints. This commentary discusses a recent application leveraging machine learning to predict one-year patient survival following orthotopic heart transplantation. This modeling approach has significant implications in terms of improving clinical decision-making, patient counseling, and ultimately organ allocation and has been shown to significantly outperform pre-existing algorithms. This commentary also discusses how adoption and advancement of this modeling approach in the future can provide increased personalization of patient care. The continued expansion of information systems and growth of electronic patient data sources in health care will continue to pave the way for increased use and adoption of data science in medicine. Personalized medicine has been a long-standing goal of the healthcare community and with machine learning and AI now being continually incorporated into clinical settings and practice, this technology is well on the pathway to make a considerable impact to greatly improve patient care in the near future.

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Background: Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditions render maintenance of glycemic control very challenging in this setting. Materials and Methods: This study involved collection of continuous glucose monitoring (CGM) data, and other associated measures, from the electronic medical records of 127 patients for the first 72 h of ICU care who upon admission to the ICU had a diagnosis of type 1 (n = 8) or type 2 diabetes (n = 97) or a glucose value >150 mg/dL (n = 22). A neural network-based model was developed to predict a complete trajectory of glucose values up to 135 min ahead of time. Model accuracy was validated using data from 15 of the 127 patients who were not included in the model training set to simulate model performance in real-world health care settings. Results: Predictive models achieved an improved accuracy and performance compared with previous models that were reported by our research team. Model error, expressed as mean absolute difference percent, was 10.6% with respect to interstitial glucose values (CGM) and 15.9% with respect to serum blood glucose values collected 135 min in the future. A Clarke Error Grid Analysis of model predictions with respect to the reference CGM and blood glucose measurements revealed that >99% of model predictions could be regarded as clinically acceptable and would not lead to inaccurate insulin therapy or treatment recommendations. Conclusion: The noted clinical acceptability of these models illustrates their potential utility within a clinical decision support system to assist health care providers in the optimization of glycemic management in critical care patients.

Presumed antiphospholipid syndrome and thrombotic thrombocytopenic purpura: An infrequent association.

Antiphospholipid syndrome (APS) is an autoimmune disease that demonstrates antiphospholipid antibodies that cause hypercoagulability and leads to venous and arterial thrombosis. Autoantibodies to a disintegrin-like and metalloprotease with thrombospondin type I motif, member 13 (ADAMTS 13) play a role in the microthrombosis of thrombotic thrombocytopenic purpura in APS patients.

Continuous glucose monitoring identifies relationship between optimized glycemic control and post-discharge acute care facility needs.

OBJECTIVE: Hyperglycemia is an independent risk factor in hospitalized patients for adverse outcomes, even if patients are not diabetic. We used continuous glucose monitoring to evaluate whether glycemic control (hyperglycemia) in the first 72 h after an intensive care admission was associated with the need for admission to a post discharge long-term medical facility. RESULTS: We enrolled 59 coronary artery bypass grafting patients. Poor glycemic control was defined as greater than 33% of continuous glucose monitoring values < 70 and > 180 mg/dL (group 1); and then these patients were reevaluated with a less strict definition of poor glycemic control with greater than 25% of continuous glucose values < 70 and > 180 mg/dL (group 2). In group 1 4/10 (40.0%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 6/49 (12.2%) that were well controlled. In reevaluation as group 2, 5/14 (35.7%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 5/45 (11.1%) who were well controlled. Admission to a post discharge facility was increased in patients with poor glycemic control p = 0.045 and p = 0.042 for group 1 and group 2, and with odds ratios of 4.8 (95% CI 1.0-22.5) and 4.4 (95% CI 1.0-19.4), respectively.

Increasing patient safety with neonates via handoff communication during delivery: a call for interprofessional health care team training across GME and CME.

Hospitals have struggled for years regarding the handoff process of communicating patient information from one health care professional to another. Ineffective handoff communication is recognized as a serious patient safety risk within the health care community. It is essential to take communication into consideration when examining the safety of neonates who require immediate medical attention after birth; effective communication is vital for positive patient outcomes, especially with neonates in a delivery room setting. Teamwork and effective communication across the health care continuum are essential for providing efficient, quality care that leads to favorable patient outcomes. Interprofessional simulation and team training can benefit health care professionals by improving interprofessional competence, defined as one's knowledge of other professionals including an understanding of their training and skillsets, and role clarity. Interprofessional teams that include members with specialization in obstetrics, gynecology, and neonatology have the potential to considerably benefit from training effective handoff and communication practices that would ensure the safety of the neonate upon birth. We must strive to provide the most comprehensive systematic, standardized, interprofessional handoff communication training sessions for such teams, through Graduate Medical Education and Continuing Medical Education that will meet the needs across the educational continuum.

Comorbidity polypharmacy score and its clinical utility: A pragmatic practitioner's perspective.

Modern medical management of comorbid conditions has resulted in escalating use of multiple medications and the emergence of the twin phenomena of multimorbidity and polypharmacy. Current understanding of how the polypharmacy in conjunction with multimorbidity influences trauma outcomes is limited, although it is known that trauma patients are at increased risk for medication-related adverse events. The comorbidity-polypharmacy score (CPS) is a simple clinical tool that quantifies the overall severity of comorbidities using the polypharmacy as a surrogate for the "intensity" of treatment necessary to adequately control chronic medical conditions. Easy to calculate, CPS is derived by counting all known pre-injury comorbid conditions and medications. CPS has been independently associated with mortality, increased risk for complications, lower functional outcomes, readmissions, and longer hospital stays. In addition, CPS may help identify older trauma patients at risk of post-emergency department undertriage. The goal of this article was to review and refine the rationale for CPS and to provide an evidence-based outline of its potential clinical applications.

Evaluation of a model for glycemic prediction in critically ill surgical patients.

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies.

Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.

BACKGROUND: Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose. METHODS: A feed-forward NNM was designed for real-time prediction of glucose in patients with diabetes implementing a prediction horizon of 75 min. Inputs to the NNM included CGM values, insulin dosages, metered glucose values, nutritional intake, lifestyle, and emotional factors. Performance of the NNM was assessed in 10 patients not included in the model training set. RESULTS: The NNM had a root mean squared error of 43.9 mg/dL and a mean absolute difference percentage of 22.1. The NNM routinely overestimates hypoglycemic extremes, which can be attributed to the limited number of hypoglycemic reactions in the model training set. The model predicts 88.6% of normal glucose concentrations (> 70 and < 180 mg/dL), 72.6% of hyperglycemia (≥ 180 mg/dL), and 2.1% of hypoglycemia (≤ 70 mg/dL). Clarke Error Grid Analysis of model predictions indicated that 92.3% of predictions could be regarded as clinically acceptable and not leading to adverse therapeutic direction. Of these predicted values, 62.3% and 30.0% were located within Zones A and B, respectively, of the error grid. CONCLUSIONS: Real-time prediction of glucose via the proposed NNM may provide a means of intelligent therapeutic guidance and direction.

Development of a neural network model for predicting glucose levels in a surgical critical care setting.

Development of neural network models for the prediction of glucose levels in critically ill patients through the application of continuous glucose monitoring may provide enhanced patient outcomes. Here we demonstrate the utilization of a predictive model in real-time bedside monitoring. Such modeling may provide intelligent/directed therapy recommendations, guidance, and ultimately automation, in the near future as a means of providing optimal patient safety and care in the provision of insulin drips to prevent hyperglycemia and hypoglycemia.

Development of a neural network for prediction of glucose concentration in type 1 diabetes patients.

BACKGROUND: A major difficulty in the management of diabetes is the optimization of insulin therapies to avoid occurrences of hypoglycemia and hyperglycemia. Many factors impact glucose fluctuations in diabetes patients, such as insulin dosage, nutritional intake, daily activities and lifestyle (e.g., sleep-wake cycles and exercise), and emotional states (e.g., stress). The overall effect of these factors has not been fully quantified to determine the impact on subsequent glycemic trends. Recent advances in diabetes technology such as continuous glucose monitoring (CGM) provides significant sources of data, such that quantification may be possible. Depending on the CGM technology utilized, the sampling frequency ranges from 1-5 min. In this study, an intensive electronic diary documenting the factors previously described was created. This diary was utilized by 18 patients with insulin-dependent diabetes mellitus in conjunction with CGM. Utilizing this dataset, various neural network models were constructed to predict glucose in these diabetes patients while varying the predictive window from 50-180 min. The predictive capability of each neural network within the fully trained dataset was analyzed as well as the predictive capabilities of the neural networks on unseen data. METHODS: Neural network models were created using NeuroSolutions software with variable predictive windows of 50, 75, 100, 120, 150, and 180 min. Neural network models were trained using patient datasets ranging from 11-17 patients and evaluated on patient data not included in the neural network formulation. Performance analysis was completed for the neural network models using MATLAB. Performance measures include the calculation of the mean absolute difference percent overall and at hypoglycemic and hyperglycemic extremes, and the percentage of hypoglycemic and hyperglycemic occurrences were predicted. RESULTS: Overall, the neural network models perform adequately at predicting at normal (>70 and <180 mg/dl) and hyperglycemic ranges (> or =180 mg/dl); however, glucose concentrations in areas of hypoglycemia were commonly overestimated. One potential reason for the "high" predictions in areas of hypoglycemia is due to the minimal occurrences of hypoglycemic events within the training data. The entire 18-patient dataset (consisting of 18,400 glucose values) had a relatively low incidence of hypoglycemia (1460 CGM values < or =70 mg/dl), which corresponds to approximately 7.9% of the dataset. On the contrary, hyperglycemia comprised approximately 35.7% of the dataset (6560 CGM values >or =180 mg/dl), and euglycemic values allotted for 56.4% of the dataset (10,380 CGM values >70 and <180 mg/dl). Results further indicate that an increase in predictive window leads to a decrease in predictive accuracy of the neural network model. It is hypothesized that the underestimation of hyperglycemic extremes is due to the extension of the predictive window and the associated inability of the neural network to determine oscillations and trends in glycemia as well as the occurrence of other relevant input events such as lifestyle, emotional states, insulin dosages, and meals, which may occur within the predicted time window and may impact or change neural network weights. CONCLUSIONS: In this investigation, the feasibility of utilizing neural network models for the prediction of glucose using predictive windows ranging from 50-180 min is demonstrated. The predictive windows were chosen arbitrarily to cover a wide range; however, longer predictive windows were implemented to gain a predictive view of 120-180 min, which is very important for diabetes patients, specifically after meals and insulin dosages. Neural networks, such as those generated in this investigation, could be utilized in a semiclosed-loop device for guiding therapy in diabetes patients. Use of such a device may lead to better glycemic control and subsequent avoidance of complications.