Clinical Decision Aid Research Articles

Determining consistency of care after resuscitation from in-hospital cardiac arrest, a retrospective analysis at a tertiary care academic medical center

BackgroundFew guidelines have focused on the care delivered after return of spontaneous circulation (ROSC). Post ROSC best practice guidelines lack clarity about important tasks to accomplish in the first hours after ROSC. Objectives and methodsWe conducted a retrospective cohort analysis of adults who had suffered an in hospital cardiac arrest (IHCA) with ROSC over a two-year period to determine the completion rate of critical tasks in the immediate post-ROSC period: ECG within one hour, ABG within one hour, physician documentation within six hours, and surrogate communication within six hours. ResultsIn the 113 reviewed cases, there was significant variance between completion of all four (19.4%), three (35.3%), two (32.7%), one (20.6%) and none (1.7%) of these critical post ROSC tasks. We observed that 62.8% of IHCA with ROSC had an ECG obtained within one hour of ROSC. The rate of obtaining an ABG within one hour of ROSC was 76.9%. 49.5% of cases had physician documentation of the resuscitation within six hours of ROSC. The rate of documenting surrogate communication within six hours of ROSC was 69.9%. ConclusionsOur study demonstrated that the completion rates of critical tasks in the post ROSC setting were suboptimal within our patient cohort. This provides a baseline for the development of future best practice guidelines and clinical decision-making aids for post ROSC care after IHCA. This can lead to future research in coupling specific care tasks to post ROSC patient outcomes.

Extracellular Matrix-Based Gene Expression Signature Defines Two Prognostic Subtypes of Hepatocellular Carcinoma With Different Immune Microenvironment Characteristics

Background: Accumulating evidence has suggested that the extracellular matrix (ECM) plays a vital role in the development and progression of cancer, and could be recognized as a biomarker of the response to immunotherapy. However, the effect of the ECM signature in hepatocellular carcinoma (HCC) is not well understood. Methods: HCC patients derived from the TCGA-LIHC dataset were clustered according to the ECM signature. The differences in prognosis, functional enrichment, immune infiltration, and mutation characteristics between distinct molecular clusters were examined, and its predictive value on the sensitivities to chemotherapy and immunotherapy was further analyzed. Then, a prognostic model was built based on the ECM-related gene expression pattern. Results: HCC patients were assigned into two molecular subtypes. Approximately 80% of HCC patients were classified into cluster A with poor prognosis, more frequent TP53 mutation, and lower response rate to immunotherapy. In contrast, patients in cluster B had better survival outcomes and higher infiltration levels of dendritic cells, macrophages, and regulatory T cells. The prognostic risk score model based on the expression profiles of six ECM-related genes (SPP1, ADAMTS5, MMP1, BSG, LAMA2, and CDH1) demonstrated a significant association with higher histologic grade and advanced TNM stage. Moreover, the prognostic risk score showed good performance in both the training dataset and validation dataset, as well as improved prognostic capacity compared with TNM stage. Conclusions: We characterized two HCC subtypes with distinct clinical outcomes, immune infiltration, and mutation characteristics. A novel prognostic model based on the ECM signature was further developed, which may contribute to individualized prognostic prediction and aid in clinical decision-making.

Development and Validation of Nomograms to Predict Overall Survival and Cancer-Specific Survival in Patients With Pancreatic Adenosquamous Carcinoma.

BackgroundPancreatic adenosquamous carcinoma (PASC) is a heterogeneous group of primary pancreatic cancers characterized by the coexistence of both glandular and squamous differentiation. The aim of this study was to develop nomograms to predict survival outcomes in patients with PASC.MethodsIn this retrospective study, data on PASC, including clinicopathological characteristics, treatments, and survival outcomes, were collected from the SEER database between 2000 and 2018. The primary endpoints were overall survival (OS) and cancer-specific survival (CSS). The eligible patients were randomly divided into development cohort and validation cohort in a 7:3 ratio. The nomograms for prediction of OS and CSS were constructed by the development cohort using a LASSO-Cox regression model, respectively. Besides the model performance was internally and externally validated by examining the discrimination, calibration, and clinical utility.ResultsA total of 632 consecutive patients who had been diagnosed with PASC were identified and randomly divided into development (n = 444) and validation (n = 188) cohorts. In the development cohort, the estimated median OS was 7.0 months (95% CI: 6.19–7.82) and the median CSS was 7.0 months (95% CI: 6.15–7.85). In the validation cohort, the estimated median OS was 6.0 months (95% CI: 4.46–7.54) and the median CSS was 7.0 months (95% CI: 6.25–7.75). LASSO-penalized COX regression analysis identified 8 independent predictors in the OS prediction model and 9 independent risk factors in the CSS prediction model: age at diagnosis, gender, year of diagnosis, tumor location, grade, stage, size, lymph node metastasis, combined metastasis, surgery, radiation, and chemotherapy. The Harrell C index and time-dependent AUCs manifested satisfactory discriminative capabilities of the models. Calibration plots showed that both models were well calibrated. Furthermore, decision curves indicated good utility of the nomograms for decision-making.ConclusionNomogram-based models to evaluate personalized OS and CSS in patients with PASC were developed and well validated. These easy-to-use tools will be useful methods to calculate individualized estimate of survival, assist in risk stratification, and aid clinical decision-making.

Exploring remedies for defective artificial intelligence aids in clinical decision-making in post-Brexit England and Wales

Artificially intelligent systems (AISs) are in development to aid the clinicians and patients of the United Kingdom’s National Health Service. We assess the statutory requirements for product liability claims against producers of defective AISs in clinical use and set out the criteria for bringing a successful claim against a producer to the courts of England and Wales. We argue that the mismatch between product liability and safety regulation leaves patients, and consumers more generally, without an adequate remedy for the consequences of AIS defects. We also discuss the intertwinement of the consumer Protection Act 1987 and the Medical Devices Regulations 2002. Recent developments such as United Kingdom’s withdrawal from the European Union and the updated Medicines and Medical Devices Act 2021 are discussed. In addition, we offer novel discussion regarding the tort of ‘breach of statutory duty’ as provided for by the Medicines and Medical Devices Act 2021.

Key considerations for the implementation of clinically focused Prescription Drug Monitoring Programs to avoid unintended consequences.

Prescription Drug Monitoring Programs (PDMP) are electronic databases that are used to track and monitor the prescribing and dispensing of controlled substances, including opioid analgesics and benzodiazepines. PDMP have been widely implemented throughout North America and are currently being introduced in Australia and some parts of Europe. PDMP were originally developed by and for law enforcement, however many jurisdictions have now shifted use toward clinical care and harm reduction through early identification of prescription dependence and extra-medical use, and to ensure appropriate supply of controlled substances to the community through monitoring health care provider prescribing and dispensing patterns (Deloitte, 2018; Picco etal., 2021a; CDC,2021a, U.S Department of Justice, 2018). Clinically-motivated PDMP highlight medication-related risk, based on the patient's prescribing and dispensing history. Health care professionals can use this information to aid or inform clinical decision-making and provide opportunities for intervention and treatment (Deloitte, 2018) . However, a number of harms have been associated with the use of PDMP, including increased stigma and discrimination, untreated pain and mental illness, and denial of appropriate health care for those identified as 'high risk'. In this article we examine these harms and potential mitigating factors. We conclude with some suggestions and future directions for research to address some of the current uncertainties regarding PDMP use. We highlight the need for mixed methods research to better understand the personal impacts of PDMP policy on the populations they were designed to aid.

A clinical decision aid to discern patients without and with midfacial and mandibular fractures that require treatment (the REDUCTION-II study): a prospective multicentre cohort study

PurposeTo assess the diagnostic accuracy of physical examination findings and to construct clinical decision aids to discern emergency department patients without and with midfacial and mandibular fractures that require treatment.MethodsA prospective multicentre cohort study was conducted in four hospitals in the Netherlands. Consecutive maxillofacial trauma patients were included whereupon each patient underwent a standardized physical examination consisting of 15 and 14 findings for midfacial and mandibular trauma, respectively. The primary outcome was the decision whether to treat during the emergency department stay or within 24 h of admission. The diagnostic accuracy was calculated for the individual physical examination findings and ensuing clinical decision aids with the focus being on detecting midfacial and mandibular fractures that require active treatment.ResultsA total of 766 midfacial trauma patients were identified of whom 339 (44.3%) had midfacial fractures. Of those, 74 (21.8%) required active treatment. A total of 280 mandibular trauma patients were identified of whom 66 (23.6%) had mandibular fractures. Of those, 37 (56.0%) required active treatment. The decision aid for midfacial trauma consisting of facial depression, epistaxis, ocular movement limitation, palpable step-off, objective malocclusion and tooth mobility or avulsion had a sensitivity of 97.3 (90.7–99.3), a specificity of 38.6 (35.0–42.3), and a negative predictive value of 99.3 (97.3–99.8). The decision aid for mandibular trauma consisting of mouth opening limitation, jaw movement pain, objective malocclusion and tooth mobility or avulsion resulted in a sensitivity of 100.0 (90.6–100.0), a specificity of 39.1 (33.2–45.4), and a negative predictive value of 100.0 (96.1–100.0).ConclusionThe clinical decision aids successfully identified midfacial and mandibular trauma patients requiring active fracture treatment and so may be useful in preventing unnecessary radiological procedures in the future.Trial RegistrationThe study was registered at ClinicalTrials.gov with the identifier NCT03314480.

Supervision and Care Quality as Perceived by Redeployed Attendings, Fellows, and Residents During a COVID-19 Surge: Lessons for the Future.

PurposeTo better prepare for potential future large-scale redeployments, this study examines quality of supervision and care as perceived by redeployed residents, fellows, and attendings during a COVID-19 surge.MethodDuring April and May 2020, attendings, fellows, and residents redeployed at 2 teaching hospitals were invited to participate in a survey, which included questions on respondents’ prior experience; redeployed role; amount of supervision needed and received; and perceptions of quality of supervision, patient care, and interprofessional collaboration. Frequencies, means, and P values were calculated to compare perceptions by experience and trainee status. Narrative responses to 2 open-ended questions were independently coded; themes were constructed.ResultsOverall, 152 of 297 (51.2%) individuals responded, including 64 of 142 attendings (45.1%), 40 of 79 fellows (50.6%), and 48 of 76 residents (63.2%). Fellows and attendings, regardless of prior experience, perceived supervision as adequate. In contrast, experienced residents reported receiving more supervision than needed, while inexperienced residents reported receiving less supervision than needed and rated overall supervision as poor. Attendings, fellows, and experienced residents rated the overall quality of care as acceptable to good, whereas inexperienced residents perceived overall quality of care as worse to much worse, particularly when compared with baseline.ConclusionsNarrative themes indicated that the quality of supervision and care was buffered by strong camaraderie, a culture of informal consultation, team composition (mixing experienced with inexperienced), and clinical decision aids. The markedly negative view of inexperienced residents suggests a higher risk for disillusionment, perhaps even moral injury, during future redeployments. Implications for planning are explored.

The diagnostic accuracy of prehospital triage tools in identifying patients with traumatic brain injury: A systematic review

IntroductionPrehospital care providers are usually the first responders for patients with traumatic brain injury (TBI). Early identification of patients with TBI enables them to receive trauma centre care, which improves outcomes. Two recent systematic reviews concluded that prehospital triage tools for undifferentiated major trauma have low accuracy. However, neither review focused specifically on patients with suspected TBI. Therefore, we aimed to systematically review the existing evidence on the diagnostic performance of prehospital triage tools for patients with suspected TBI. MethodsA comprehensive search of the current literature was conducted using Medline, EMBASE, CINAHL Plus and the Cochrane library (inception to 1st June 2021). We also searched Google Scholar, OpenGrey, pre-prints (MedRxiv) and dissertation databases. We included all studies published in English language evaluating the accuracy of prehospital triage tools for TBI. We assessed methodological quality and risk of bias using a modified Quality Assessment of Diagnostic Studies (QUADAS-2) tool. Two reviewers independently performed searches, screened titles and abstracts and undertook methodological quality assessments. Due to the heterogeneity in the population of interest and prehospital triage tools used, a narrative synthesis was undertaken. ResultsThe initial search identified 1787 articles, of which 8 unique eligible studies met the inclusion criteria (5 retrospective, 2 prospective, 1 mixed). Overall, sensitivity of triage tools studied ranged from 19.8% to 87.9% for TBI identification. Specificity ranged from 41.4% to 94.4%. Two decision tools have been validated more than once: HITS-NS (2 studies, sensitivity 28.3–32.6%, specificity 89.1–94.4%) and the Field Triage Decision Scheme (4 studies, sensitivity 19.8–64.5%, specificity 77.4%-93.1%). Existing tools appear to systematically under-triage older patients. ConclusionFurther efforts are needed to improve and optimise prehospital triage tools. Consideration of additional predictors (e.g., biomarkers, clinical decision aids and paramedic judgement) may be required to improve diagnostic accuracy.

Ratio of Mixed Venous Oxygen Saturation-to-Pulmonary Capillary Wedge Pressure: Insights From the Veterans Affairs Clinical Assessment, Reporting, and Tracking Program.

Hemodynamic values from right heart catheterization aid diagnosis and clinical decision-making but may not predict outcomes. Mixed venous oxygen saturation percentage and pulmonary capillary wedge pressure relate to cardiac output and congestion, respectively. We theorized that a novel, simple ratio of these measurements could estimate cardiovascular prognosis. We queried Veterans Affairs' databases for clinical, hemodynamic, and outcome data. Using the index right heart catheterization between 2010 and 2016, we calculated the ratio of mixed venous oxygen saturation-to-pulmonary capillary wedge pressure, termed ratio of saturation-to-wedge (RSW). The primary outcome was time to all-cause mortality; secondary outcome was 1-year urgent heart failure presentation. Patients were stratified into quartiles of RSW, Fick cardiac index (CI), thermodilution CI, and pulmonary capillary wedge pressure alone. Kaplan-Meier curves and Cox proportional hazards models related comparators with outcomes. Of 12 019 patients meeting inclusion criteria, 9826 had values to calculate RSW (median 4.00, interquartile range, 2.67-6.05). Kaplan-Meier curves showed early, sustained separation by RSW strata. Cox modeling estimated that increasing RSW by 50% decreases mortality hazard by 19% (estimated hazard ratio, 0.81 [95% CI, 0.79-0.83], P<0.001) and secondary outcome hazard by 28% (hazard ratio, 0.72 [95% CI, 0.70-0.74], P<0.001). Among the 3793 patients with data for all comparators, Cox models showed RSW best associated with outcomes (by both C statistics and Bayes factors). Furthermore, pulmonary capillary wedge pressure was superior to thermodilution CI and Fick CI. Multivariable adjustment attenuated without eliminating the association of RSW with outcomes. In a large national database, RSW was superior to conventional right heart catheterization indices at assessing risk of mortality and urgent heart failure presentation. This simple calculation with routine data may contribute to clinical decision-making in this population.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity

Study objectiveA multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participantsCore team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. ResultsThe team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. ConclusionLessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach

It has been reported that the healthcare industry is the slowest adopter of artificial intelligence methods, particularly machine learning (ML), compared to other industries. However, ML can provide unprecedented opportunities for clinical decision-making aid that help improve treatment outcomes and enhance cost-effectiveness. This method paper aims to identify the enablers for adopting ML in supporting clinical decision-making and propose a strategic road map toward boosting the clinicians' intentions to adopt ML as a clinical decision support tool. This paper utilizes the Total Interpretive Structural Modeling (TISM) methodology and the Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis to investigate the relationships and the interaction between the identified enablers and to develop a hierarchical model that helps policymakers and the other key stakeholders devise the necessary strategies to enhance the adoption of ML in supporting clinical decision-making. The paper concludes that building an academic foundation, raising awareness among the clinicians and patients, building trust in machine learning, and enhancing the perceived normative congruence are among the most important enablers for boosting the clinicians' intentions to adopt machine learning in supporting clinical decision-making.

Diagnostic performance of the RSNA-proposed classification for COVID-19 pneumonia versus pre-pandemic controls

ObjectiveTo evaluate the diagnostic accuracy of the Radiological Society of North America (RSNA) classification system for coronavirus disease 2019 (COVID-19) pneumonia compared to pre-pandemic chest computed tomography (CT) scan images to mitigate the risk of bias regarding the reference standard. Materials and methodsThis was a retrospective, cross-sectional, diagnostic test accuracy study. Chest CT scans, carried out from May 1 to June 30, 2020, and from May 1 to July 17, 2017, were consecutively selected for the COVID-19 (positive reverse transcription-polymerase chain reaction [RT-PCR] for severe acute respiratory syndrome coronavirus 2 result) and control (pre-pandemic) groups, respectively. Four expert thoracic radiologists blindly interpreted each CT scan image. Sensitivity and specificity were calculated. ResultsA total of 160 chest CT scan images were included: 79 in the COVID-19 group (56 [43.5–67] years old, 41 men) and 81 in the control group (62 [52–72] years old, 44 men). Typically, an estimated specificity of 98.5% (95% confidence interval [CI] 98.1%–98.4%) was obtained. For the indeterminate classification as a diagnostic threshold, an estimated sensitivity of 88.3% (95% CI 84.7%–91.7%) and a specificity of 79.0% (95% CI 74.5%–83.4%), with an area under the curve of 0.865 (95% CI 0.838–0.895), were obtained. ConclusionThe RSNA classification system shows strong diagnostic accuracy for COVID-19 pneumonia, even against pre-pandemic controls. It can be an important aid in clinical decision-making, especially when a typical or indeterminate pattern is found, possibly advising retesting following an initial negative RT-PCR result and streamlining early management and isolation.

Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients

Machine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice. To assess the ability of machine learning algorithms to predict dementia incidence within 2 years compared with existing models and determine the optimal analytic approach and number of variables required. This prognostic study used data from a prospective cohort of 15 307 participants without dementia at baseline to perform a secondary analysis of factors that could be used to predict dementia incidence. Participants attended National Alzheimer Coordinating Center memory clinics across the United States between 2005 and 2015. Analyses were conducted from March to May 2021. 258 variables spanning domains of dementia-related clinical measures and risk factors. The main outcome was incident all-cause dementia diagnosed within 2 years of baseline assessment. In a sample of 15 307 participants (mean [SD] age, 72.3 [9.8] years; 9129 [60%] women and 6178 [40%] men) without dementia at baseline, 1568 (10%) received a diagnosis of dementia within 2 years of their initial assessment. Compared with 2 existing models for dementia risk prediction (ie, Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score, and the Brief Dementia Screening Indicator), machine learning algorithms were superior in predicting incident all-cause dementia within 2 years. The gradient-boosted trees algorithm had a mean (SD) overall accuracy of 92% (1%), sensitivity of 0.45 (0.05), specificity of 0.97 (0.01), and area under the curve of 0.92 (0.01) using all 258 variables. Analysis of variable importance showed that only 6 variables were required for machine learning algorithms to achieve an accuracy of 91% and area under the curve of at least 0.89. Machine learning algorithms also identified up to 84% of participants who received an initial dementia diagnosis that was subsequently reversed to mild cognitive impairment or cognitively unimpaired, suggesting possible misdiagnosis. These findings suggest that machine learning algorithms could accurately predict incident dementia within 2 years in patients receiving care at memory clinics using only 6 variables. These findings could be used to inform the development and validation of decision-making aids in memory clinics.

Prediction models of treatment response in lupus nephritis

In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three-quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.

Primary and recurrent patellar dislocation: recommendation fordiagnostic evaluation, risk stratification and treatment

The dogma that (every) primary patellar dislocation should be treated non-operatively is considered outdated. Therefore, every first-time dislocation, as well as every recurrent dislocation, should be fully diagnosed with regard to the injury pattern and anatomically predisposing risk factors. This enables the use of risk stratification models to assess the risk of recurrent episodes of instability, thereby supporting the clinical decision-making process. The reconstruction of the MPFL can be considered an established and successful treatment strategy for patellar instability. However, the additional correction of bony risk factors is often useful. In this context, the preoperative grading of the Jsign and dynamic assessment of patellar instability (dynamic apprehension test) are of utmost importance. These findings, combined with the radiographic imaging findings, can be used as a clinical decision aid for a bony correction procedure.

A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis

BackgroundPersonalized treatment for clinical T1 renal cortical masses (RCMs) should take into account competing risks related to tumor and patient characteristics. ObjectiveTo develop treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-d Clavien grade ≥3 complications across radical nephrectomy (RN), partial nephrectomy (PN), thermal ablation (TA), and active surveillance (AS). Design, setting, and participantsPretreatment clinical and radiological features were collected for consecutive adult patients treated with initial RN, PN, TA, or AS for RCMs at four high-volume referral centers (2000–2019). Outcome measurements and statistical analysisPrediction models used competing-risks regression for CSM and OCM and logistic regression for 90-d Clavien grade ≥3 complications. Performance was assessed using bootstrap validation. Results and limitationsThe cohort comprised 5300 patients treated with RN (n = 1277), PN (n = 2967), TA (n = 476), or AS (n = 580). Over median follow-up of 5.2 yr (interquartile range 2.5–8.7), there were 117 CSM, 607 OCM, and 198 complication events. The C index for the predictive models was 0.80 for CSM, 0.77 for OCM, and 0.64 for complications. Predictions from the fitted models are provided in an online calculator (https://small-renal-mass-risk-calculator.fredhutch.org). To illustrate, a hypothetical 74-yr-old male with a 4.5-cm RCM, body mass index of 32 kg/m2, estimated glomerular filtration rate of 50 ml/min, Eastern Cooperative Oncology Group performance status of 3, and Charlson comorbidity index of 3 has predicted 5-yr CSM of 2.9–5.6% across treatments, but 5-yr OCM of 29% and risk of 90-d Clavien grade 3–5 complications of 1.9% for RN, 5.8% for PN, and 3.6% for TA. Limitations include selection bias, heterogeneity in practice across treatment sites and the study time period, and lack of control for surgeon/hospital volume. ConclusionsWe present a risk calculator incorporating pretreatment features to estimate treatment-specific competing risks of mortality and complications for use during shared decision-making and personalized treatment selection for RCMs. Patient summaryWe present a risk calculator that generates personalized estimates of the risks of death from cancer or other causes and of complications for surgical, ablation, and surveillance treatment options for patients with stage 1 kidney tumors.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Development and preliminary evaluation of tablet computer-based decision aid for patients participating in cancer clinical trials

ObjectivesPatients often consent to participate in cancer clinical trials despite misunderstanding the trial content. We developed a tablet-based clinical trial decision aid and tested its use with the usual discussion at the time of clinical trial registration. MethodsParticipants were individuals considering participating in a breast cancer clinical trial. The control participated in usual discussions; the intervention group participated in discussion using the decision aid. Pre- and post-discussion, we investigated knowledge, decision-making conflict, and discussion length. ResultsWe enrolled 54 patients, 27 in the control group and 27 in the intervention group. Post-discussion clinical trial knowledge was significantly higher in the intervention group than in the control group (p = 0.003). No significant difference was found in decisional conflict, but the intervention group tended to have lower post-discussion conflict than the control group. There was no between-group difference in the length of discussions with physicians and clinical research coordinators. ConclusionFor women considering participation in cancer clinical trials, a tablet-based decision aid may promote clinical trial understanding without increasing discussion length or patient burden. This pre-learning decision aid incorporating a quiz and bidirectional question prompt lists may improve participants’ understanding of clinical trials.

Exploring patient satisfaction of a joint-consultation clinic for trigeminal neuralgia: Enabling improved decision-making.

Trigeminal neuralgia (TN) is a relatively rare condition which has a profound impact not only on the patient but also on those around them. There is no cure for TN, and the management of the condition is complex. The most effective forms of treatment are either through medication, neurosurgery, or combination of the two. Each option has risks and implications for the patient. As with all clinical decisions, it is important for patients to understand and be fully informed of the treatments available to them. A London UK unit adopted a joint-consultation clinic approach where the patient meets with both physician and neurosurgeon at the same time to discuss treatment options. The purpose of this evaluation is to understand patients' level of satisfaction with the joint-consultation clinic and evaluate utilisation of a clinical decision-making tool. Patients who had attended the joint-consultation clinic over a period of 12months were invited to participate in a telephone or paper survey (N = 55). Responses were analysed using descriptive statistics and thematic analysis. Forty-one patients (77% response rate) participated in the survey, and the results were overwhelmingly positive for the joint-consultation clinic regarding satisfaction. The benefits were broad ranging including increased understanding, collaboration and confidence in decision-making. A joint-consultation clinic comprising a neurosurgeon and a physician for the treatment of TN is valued by patients who become better informed and able to make decisions about their care. Positive application of clinical decision-making aids in this situation offers potential across specialities.

61 Gestalt in Acute Trauma: Evaluating Clinician Accuracy in Predicting Abdominal Injuries Among Trauma Patients

We sought to evaluate the accuracy of physician gestalt in predicting clinically significant abdominal injuries among trauma patients presenting to the emergency department (ED) by comparing physician predictions to patient outcomes based on computed tomography (CT) reports and hospital course, including admissions and procedures. As part of an observational multi-center prospective study of CT use in acute abdominal trauma, we surveyed physicians regarding their estimated likelihood of clinically significant injuries. The study was conducted from January 2017 - March 2020 at EDs within three major trauma centers. Patients presenting with blunt trauma who received abdominal CTs were included in the study. Exclusion criteria consisted of penetrating trauma, presentation to ED > 72 hours after trauma, age <15, occurrence of emergency procedures prior to abdominal radiography, and viewing of radiographic scans or reports by physicians prior to patient evaluation. The primary outcome was clinically significant injury, defined as an injury on CT requiring either hospital admission or procedure. The primary predictor was clinician gestalt: physicians were asked to estimate the likelihood of clinically significant injury, with responses including <2%, 2-10%, 10-20%, 20-40%, >40%. We calculated sensitivity, specificity, interval likelihood ratios (LR), injury proportions, and the area under the receiver operating characteristic curve (AUROC). Of 1677 patients, 326 (19%) had an injury on CT and 222 (13%) had a clinically significant injury. The proportion of clinically significant injuries increased with estimated risk of injury: of those estimated to have <2% risk of injury, 5.6% had a clinically significant injury and among those estimated to have >40% risk, 29.8% had a clinically significant injury. When comparing physician predictions across estimates, the interval LR associated with clinically significant injuries increased with increasing suspicion of injury, with the interval LR for <2% likelihood of injury at 0.38, the LR for 2-10% at 0.45, the LR for 10-20% at 1.2, the LR for 20-40% at 1.4 and the LR for >40% at 2.8. The sensitivity for detecting clinically significant injuries using <2% as the threshold for low risk of injury was 95%, while the sensitivity for detecting clinically significant injuries using <10% as the threshold was 75%. Employing a <2% threshold, attending physicians showed 100% sensitivity and resident physicians showed 95% sensitivity. Using a <10% threshold, the sensitivity was 100% among attending physicians and 73% among resident physicians. Clinician gestalt tracked monotonically with patient outcomes, but physicians overestimated likelihood of clinically significant injuries at the high end of predictions and underestimated likelihood at the low end of predictions. Results showed that clinically significant injuries were found even when clinicians predicted the likelihood of injury as <2%, which suggests that physician gestalt may not demonstrate sufficient sensitivity in detecting clinically significant injuries and should not be solely relied upon to determine whether or not to CT trauma patients. A clinical decision aid demonstrating higher accuracy than physician gestalt in predicting clinically significant injuries may help reduce the number of missed cases.View Large Image Figure ViewerDownload Hi-res image Download (PPT)