Prediction of 30-day, 90-day, and 1-year mortality after colorectal cancer surgery using a data-driven approach.

PURPOSE: To develop prediction models for short-term mortality risk assessment following colorectal cancer surgery. METHODS: Data was harmonized from four Danish observational health databases into the Observational Medical Outcomes Partnership Common Data Model. With a data-driven approach using the Least Absolute Shrinkage and Selection Operator logistic regression on preoperative data, we developed 30-day, 90-day, and 1-year mortality prediction models. We assessed discriminative performance using the area under the receiver operating characteristic and precision-recall curve and calibration using calibration slope, intercept, and calibration-in-the-large. We additionally assessed model performance in subgroups of curative, palliative, elective, and emergency surgery. RESULTS: A total of 57,521 patients were included in the study population, 51.1% male and with a median age of 72 years. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.88, 0.878, and 0.861 for 30-day, 90-day, and 1-year mortality, respectively, and a calibration-in-the-large of 1.01, 0.99, and 0.99. The overall incidence of mortality were 4.48% for 30-day mortality, 6.64% for 90-day mortality, and 12.8% for 1-year mortality, respectively. Subgroup analysis showed no improvement of discrimination or calibration when separating the cohort into cohorts of elective surgery, emergency surgery, curative surgery, and palliative surgery. CONCLUSION: We were able to train prediction models for the risk of short-term mortality on a data set of four combined national health databases with good discrimination and calibration. We found that one cohort including all operated patients resulted in better performing models than cohorts based on several subgroups.

Accuracy of triage systems for mass casualty incidents in live simulations - a systematic review.

INTRODUCTION: In mass casualty incidents, insufficient triage may lead to increased morbidity and mortality due to delayed evacuation and treatment of the most critically injured patients. We report current litterature findings on accuracy of primary prehospital triage systems for mass casualty incidents in full-scale live simulations and map the challenges that lie ahead for finding the most accurate triage system. METHODS: This study was registered with PROSPERO ID: CRD42018091889. We searched the databases EMBASE, MEDLINE, Central, Web of Science, Reference lists, Scopus, ClinicalTrials.gov and Google Scholar. We included primary triage systems, studies reporting accuracy or outcomes convertible to accuracy and studies performed in full-scale live simulations. We excluded studies using paediatric, chemical, biological, radiological or nuclear populations or triage systems. Bias rating was based on a modified version of the QUADAS-2. RESULTS: A total of 15 studies were included. Six of 41 existing triage methods were tested. The studies showed substantial heterogeneity in both study characteristics and findings. Different reference standards were applied and most were based on author-defined triage categories. All studies carried an unclear-to-high risk of bias. Therefore, no quantitative comparisons were made. CONCLUSION: In general, the studies suffered from substantial heterogeneity and risk of bias. A standardised protocol for future live simulations is needed to encourage consistent and comparable data collection. We identified some of the most important topics to address in such a protocol.

Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning.

PURPOSE: Develop a prediction model to determine the probability of no lymph node metastasis (pN0) in patients with colorectal cancer. METHODS: We used data from four Danish health databases on patients with colorectal cancer diagnosed between 2001 and 2019. The registries were harmonized into one common data model (CDM). Patients with clinical T4 tumors, undergoing palliative or acute surgery, and patients undergoing neoadjuvant therapy were excluded. Preoperative data was used to train the model. A postoperative model including tumor-specific variables potentially available after local tumor resection was also developed. Additionally, both models were compared with a model based on age, sex, and clinical N stage to resemble current standards. A Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis for prediction was used. RESULTS: In total, 35,812 patients with 16,802 variables were identified in the CDM, and 194 variables affected the probability of pN0 preoperative. The area under the receiver operating characteristic curve (AUROC) was 0.64 (95% CI 0.63-0.66), and the area under the precision-recall curve (AUPRC) was 0.75 (95% CI 0.74-0.76). The mean predicted risk was 0.649, observed risk was 0.650, and calibration-in-large was 0.998. Adding histopathological data from the tumor improved the model slightly by increasing AUROC to 0.69. In comparison, the AUROC of the current standard clinical staging model was 0.57. CONCLUSION: Using Danish National Patient Registry data in a machine learning-based predictive model showed acceptable results and outperforms current tools for clinical staging in predicting pN0 status in patients scheduled for CRC surgery.

Accuracy of prehospital triage systems for mass casualty incidents in trauma register studies - A systematic review and meta-analysis of diagnostic test accuracy studies.

BACKGROUND: Prioritising patients in mass casualty incidents (MCI) can be extremely difficult. Therefore, triage systems are important in every emergency medical service. This study reviews the accuracy of primary triage systems for MCI in trauma register studies. METHODS: We registered a protocol at PROSPERO ID: CRD42018115438. We searched MEDLINE, EMBASE, Central, Web of Science, Scopus, Clinical Trials, Google Scholar, and reference lists for eligible studies. We included studies that both examined a primary triage system for MCI in trauma registers and provided sensitivity and specificity for critically injured vs non-critically injured as results. We excluded studies that used paediatric, chemical, biological, radiological or nuclear MCIs populations or triage systems. Finally, we calculated intra-study relative sensitivity, specificity and diagnostic odds ratio for each triage system. RESULTS: Triage Sieve (TS) significantly underperformed in relative diagnostic odds ratio (DOR) when compared to START and CareFlight (CF) (START vs TS: 19.85 vs 13.23 (p<0.0001)│CF vs TS: 23.72 vs 12.83 (p<0.0001)). There was no significant difference in DOR between TS and Military Sieve (MS) (p<0.710). Compared to START, MS and CF TS had significantly higher relative specificity (START vs TS: 93.6% vs 96.1% (p=0.047)│CF vs TS: 96% vs 95.3% (p=0.0006)│MS vs TS: 94% vs 88.3% (p=0.0002)) and lower relative sensitivity (START vs TS: 57.8% vs 34.8% (p<0.0001)│CF vs TS: 53.9% vs 34.7% (p<0.0001)│MS vs TS: 51.9% vs 35.2% p<0.0001)). CF had significantly better relative DOR than START (CF vs START: 23.56 vs 27.79 (p=0.043)). MS had significantly better relative sensitivity than CF and START (MS vs CF: 49.5% vs 38.7% (p<0.0001)│MS vs START: 49.4% vs 43.9% (p=0.01)). In contrast, CF had significantly better relative specificity than MS (MS vs CF: 91.3% vs 93.3% (p<0.0001)). The remaining comparisons did not yield any significant differences. CONCLUSION: As the included studies were at risk of bias and had heterogenic characteristics, our results should be interpreted with caution. Nonetheless, our results point towards inferior accuracy of Triage Sieve compared to START and CareFlight, and less firmly point towards superior accuracy of Military Sieve compared to START, CareFlight and Triage Sieve.

Efficacy, tolerability, and safety of erenumab for the preventive treatment of persistent post-traumatic headache attributed to mild traumatic brain injury: an open-label study.

BACKGROUND: Calcitonin gene-related peptide (CGRP) has recently been implicated in the pathogenesis of post-traumatic headache (PTH), which raises the prospect for therapeutic use of monoclonal antibodies targeting CGRP or its receptor. Therefore, we decided to assess the efficacy, tolerability, and safety of erenumab for prevention of persistent PTH attributed to mild traumatic brain injury. METHODS: A single-center, non-randomized, single-arm, open-label study of erenumab for adults aged 18-65 years with persistent PTH. Patients were assigned to receive 140-mg erenumab monthly by two subcutaneous 1-mL injections, given every 4 weeks for 12 weeks. The primary outcome measure was the mean change in number of monthly headache days of moderate to severe intensity from baseline (4-week pretreatment period) to week 9 through 12. Tolerability and safety endpoints were adverse events (i.e. number and type). RESULTS: Eighty-nine of 100 patients completed the open-label trial. At baseline, the mean monthly number of headache days of moderate to severe intensity was 15.7. By week 9 through 12, the number was reduced by 2.8 days. The most common adverse events were constipation (n = 30) and injection-site reactions (n = 15). Of 100 patients who received at least one dose of erenumab, two patients discontinued the treatment regimen due to adverse events. CONCLUSIONS: Among patients with persistent PTH, erenumab resulted in a lower frequency of moderate to severe headache days in this 12-week open-label trial. In addition, erenumab was well-tolerated as discontinuations due to adverse events were low. Placebo-controlled randomized clinical trials are needed to adequately evaluate the efficacy and safety of erenumab in patients with persistent PTH. TRIAL REGISTRATION: ClinicalTrials.Gov, NCT03974360. Registered on April 17, 2019 - Retrospectively registered.