Ready-To-Go Questionnaire - Development and validation of a novel medical pre-travel risk stratification tool.

BACKGROUND: There are no validated pre-travel self-assessment tools to stratify travellers' health risks and identify their needs for pre-travel medical preparation. This study presents a novel pre-travel risk stratification tool (Ready-To-Go Questionnaire). METHODS: The Ready-To-Go Questionnaire was developed by travel medical experts. It assesses information on travellers' itinerary and current health status, thereby assigning travellers to one out of four risk categories. To explore the Ready-To-Go Questionnaire's validity, we analysed the agreement between the risk categories resulting from the questionnaire and predefined validation criteria. This study was carried out at the Travel Clinic, University of Zurich, Switzerland. RESULTS: One hundred travellers attending a pre-travel consultation were included. 82% corresponded to the substantial-risk category, 17% to the high-risk category, 1% to the moderate-risk category and 0% to the low-risk category. The concordance between the risk categories and the consultants' risk assessment, was 0.39 and 0.29 (unweighted/weighted Cohen's Kappa). No significant concordance was found between the risk categories and additional validation criteria. CONCLUSION: The Ready-To-Go Questionnaire is a medical triage tool developed to identify different levels of travel-related health risks. This tool assists in better understanding travellers' needs, shaping modern travel consultations and offering patient-centred travel medicine services. CLINICAL TRIAL REGISTRATION NUMBER: ISRCTN10172086.

A Patient-Reported Outcome Tool to Triage Total Hip Arthroplasty Patients to Hospital or Video Consultation: Pilot Study With Expert Panels and a Cohort of 1228 Patients.

BACKGROUND: The digital transformation in health care has been accelerated by the COVID-19 pandemic. Video consultation has become the alternative for hospital consultation. It remains unknown how to select patients suitable for video consultation. OBJECTIVE: This study aimed to develop a tool based on patient-reported outcomes (PROs) to triage total hip arthroplasty (THA) patients to hospital or video consultation. METHODS: A pilot study with expert panels and a retrospective cohort with prospectively collected data from 1228 THA patients was executed. The primary outcome was a PRO triage tool to allocate THA patients to hospital or video consultation 6 weeks postoperatively. Expert panels defined the criteria and selected the patient-reported outcome measure (PROM) questions including thresholds. Data were divided into training and test cohorts. Distribution, floor effect, correlation, responsiveness, PRO patient journey, and homogeneity of the selected questions were investigated in the training cohort. The test cohort was used to provide an unbiased evaluation of the final triage tool. RESULTS: The expert panels selected moderate or severe pain and using 2 crutches as the triage tool criteria. PROM questions included in the final triage tool were numeric rating scale (NRS) pain during activity, 3-level version of the EuroQol 5 dimensions (EQ-5D-3L) questions 1 and 4, and Oxford Hip Score (OHS) questions 6, 8, and 12. Of the training cohort, 201 (201/703, 28.6%) patients needed a hospital consultation, which was statistically equal to the 150 (150/463, 32.4%) patients in the test cohort who needed a hospital consultation (P=.19). CONCLUSIONS: A PRO triage tool based on moderate or severe pain and using 2 crutches was developed. Around 70% of THA patients could safely have a video consultation, and 30% needed a hospital consultation 6 weeks postoperatively. This tool is promising for selecting patients for video consultation while using an existing PROM infrastructure.

Lung Ultrasonography for Risk Stratification in Patients with Coronavirus Disease 2019 (COVID-19): A Prospective Observational Cohort Study.

BACKGROUND: Lung ultrasonography (LUS) is a promising pragmatic risk-stratification tool in coronavirus disease 2019 (COVID-19). This study describes and compares LUS characteristics between patients with different clinical outcomes. METHODS: Prospective observational study of polymerase chain reaction-confirmed adults with COVID-19 with symptoms of lower respiratory tract infection in the emergency department (ED) of Lausanne University Hospital. A trained physician recorded LUS images using a standardized protocol. Two experts reviewed images blinded to patient outcome. We describe and compare early LUS findings (≤24 hours of ED presentation) between patient groups based on their 7-day outcome (1) outpatients, (2) hospitalized, and (3) intubated/dead. Normalized LUS score was used to discriminate between groups. RESULTS: Between 6 March and 3 April 2020, we included 80 patients (17 outpatients, 42 hospitalized, and 21 intubated/dead). Seventy-three patients (91%) had abnormal LUS (70% outpatients, 95% hospitalized, and 100% intubated/dead; P = .003). The proportion of involved zones was lower in outpatients compared with other groups (median [IQR], 30% [0-40%], 44% [31-70%], 70% [50-88%]; P < .001). Predominant abnormal patterns were bilateral and there was multifocal spread thickening of the pleura with pleural line irregularities (70%), confluent B lines (60%), and pathologic B lines (50%). Posterior inferior zones were more often affected. Median normalized LUS score had a good level of discrimination between outpatients and others with area under the ROC of .80 (95% CI, .68-.92). CONCLUSIONS: Systematic LUS has potential as a reliable, cheap, and easy-to-use triage tool for the early risk stratification in patients with COVID-19 presenting to EDs.

Learning from past respiratory failure patients to triage COVID-19 patient ventilator needs: A multi-institutional study.

BACKGROUND: Unlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD). METHODS: This retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019. A total of 16 cohorts were constructed, including any combinations of the four diseases plus an exploratory ARDS cohort, to determine the most appropriate cohort to use. Candidate predictors included demographic and clinical parameters that were previously associated with poor COVID-19 outcomes. Model development included the implementation of logistic regression and three ensemble tree-based algorithms: decision tree, AdaBoost, and XGBoost. Models were validated in hospitalized COVID-19 patients at two healthcare systems, March 2020-July 2020. ML models were trained on CLD patients at Stanford Hospital Alliance (SHA). Models were validated on hospitalized COVID-19 patients at both SHA and Intermountain Healthcare. RESULTS: CLD training data were obtained from SHA (n = 14,030), and validation data included 444 adult COVID-19 hospitalized patients from SHA (n = 185) and Intermountain (n = 259). XGBoost was the top-performing ML model, and among the 16 CLD training cohorts, the best model achieved an area under curve (AUC) of 0.883 in the validation set. In COVID-19 patients, the prediction models exhibited moderate discrimination performance, with the best models achieving an AUC of 0.77 at SHA and 0.65 at Intermountain. The model trained on all pneumonia and influenza cohorts had the best overall performance (SHA: positive predictive value (PPV) 0.29, negative predictive value (NPV) 0.97, positive likelihood ratio (PLR) 10.7; Intermountain: PPV, 0.23, NPV 0.97, PLR 10.3). We identified important factors associated with IMV that are not traditionally considered for respiratory diseases. CONCLUSIONS: The performance of prediction models derived from CLD for 48-hour IMV in patients hospitalized with COVID-19 demonstrate high specificity and can be used as a triage tool at point of care. Novel predictors of IMV identified in COVID-19 are often overlooked in clinical practice. Lessons learned from our approach may assist other research institutes seeking to build artificial intelligence technologies for novel or rare diseases with limited data for training and validation.

Mid-regional pro-adrenomedullin as a supplementary tool to clinical parameters in cases of suspicion of infection in the emergency department.

INTRODUCTION: Mid-regional proadrenomedullin (MR-proADM), a novel biomarker, has recently gained interest particularly with regards to its potential in assisting clinicians' decision making in patients with suspicion of infection in the emergency department (ED). A group of international experts, with research and experience in MR-proADM applications, produced this review based on their own experience and the currently available literature. AREAS COVERED: The review provides evidence related to MR-proADM as a triaging tool in avoiding unnecessary admissions to hospital and/or inadequate discharge, and identifying patients most at risk of deterioration. It also covers the use of MR-proADM in the context of COVID-19. Moreover, the authors provide a proposal on how to incorporate MR-proADM into patients' clinical pathways in an ED setting. EXPERT OPINION: The data we have so far on the application of MR-proADM in the ED is promising. Incorporating it into clinical scoring systems may aid the clinician's decision making and recognizing the 'ill looking well' and the 'well looking ill' sooner. However there are still many gaps in our knowledge especially during the ongoing COVID-19 waves. There is also a need for cost-effectiveness analysis studies especially in the era of increasing cost pressures on health systems globally.