Trajectory Curves for Purposes of Benchmarking and Predicting Clinical Outcomes: A Scoping Review

Trajectory curves are valuable tools to benchmark patient health status and predict future outcomes. A longitudinal study is underway to examine social participation after burn injury using the Life Impact Burn Recovery Evaluation (LIBRE) Profile with the goal of developing trajectory curves for specific domains that focus on social re-integration. We conducted a scoping review to inform and understand trajectory curves applied in clinical settings to compare outcomes for an individual to a matched cohort of comparable patients or predicted expected outcomes over time. This scoping review utilized a PubMed search from January 2014 to August 2019 for the following terms: “trajectory curves” or “trajectory models” and “clinic” or “clinical.” Only articles that specifically referenced longitudinal and clinical research designs were included in the scoping review. Articles were assessed using standard scoping review methods and categorized based on clinical application of trajectory curves for either benchmarking or prediction. The initial literature review identified 141 manuscripts and 34 met initial inclusion criteria. The reviewed articles support the clinical use of trajectory curves. Findings provide insight into several key determinants involved with the successful development and implementation of trajectory curves in clinical settings. These findings will inform efforts to use the LIBRE Profile to model social participation recovery and assist in developing effective strategies using trajectory curves to promote social reintegration after burn injury.

Smart Ambulance System with Remote Knowledge Communications Through Cloud

An ambulance is one of saving many lives by taking the people who need health emergencies. Saving the life of the person is one of the challenging and precious ones. Our key idea is to deliver a patient’s health condition before the victim reaches the hospital in this project. Here we use some biomedical sensors like a heartbeat sensor, temperature sensor, and a respiratory sensor to check the patient health status. There will be a continuous update to the hospital about the patient’s condition through the cloud with the help of the internet of things. The hospitals can also track the ambulance’s live location through the GPS placed in the ambulance where it arrives, and they can know at what time the patient reaches the hospital. With this information, if the patient is in critical condition, the hospital staff can make all the earlier arrangements before the patient arrives at the hospital and saves their lives as soon as possible. Here we use the biometric sensor to know the patient’s information by scanning the patient’s fingerprint. The stored database obtains this information. In cases of accident situations, to avoid legal problems, the patient’s information is sent to the cops through the GSM, and it is also intimated to the patient’s relatives as soon as possible. The parameters which are measured by using biomedical sensors are viewed by doctors using the Blynk app.

Alterations in Respiratory Function Test Three Months after Hospitalisation for COVID-19 Pneumonia: Value of Determining Nitric Oxide Diffusion

Three to four months after hospitalisation for COVID-19 pneumonia, the most frequently described alteration in respiratory function tests (RFTs) is decreased carbon monoxide transfer capacity (DLCO). Methods: This is a prospective cohort study that included patients hospitalised because of SARS-CoV-2 pneumonia, three months after their discharge. A clinical evaluation, analytical parameters, chest X-ray, six-minute walk test, spirometry and DLCO–DLNO analysis were performed. Demographic variables, comorbidities, and variables related to the severity of the admission were recorded. Results: Two hundred patients completed the study; 59.5% men, age 62 years, 15.5% admitted to the intensive care unit. The most frequent functional alteration, in 27% of patients, was in the DLCO–DLNO combination. This alteration was associated with age, male sex, degree of dyspnoea, poorer perception of health, and limited ability for physical effort. These patients also presented higher levels of D-Dimer and more residual radiological alterations. In 42% of the patients with diffusion alterations, only reduced DLNO was presented, along with lower D-Dimer levels and less capillary volume involvement. The severity of the process was associated with the reduction in DLCO–DLNO. Conclusions: The most sensitive RFT for the detection of the sequelae of COVID-19 pneumonia was the combined measurement of DLCO–DLNO and this factor was related to patient health status and their capacity for physical exertion. In 40% of these cases, there was only a reduction in DLNO, a finding that may indicate less pulmonary vascular involvement.

Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

Background When chronic conditions are associated with outcomes such as mortality, comorbidity measures are essential both to describe patient health status and to adjust for potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, as optimal comorbidity weightings remain undetermined. The present study aimed to derive a set of new population-based Elixhauser comorbidity weightings, then to validate and compare their mortality predictivity against those of the Charlson and Elixhauser-based van Walraven weightings estimates in a population-based cohort.Methods Retrospective analysis was conducted with routine Swiss general hospital (102 hospitals) data (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results for Part 1 alongside the established weighting systems used for Part 2. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Generalized additive models were weighted and adjusted for age, gender and hospital types.Results Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862–0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups and net reclassification improvement of new weights offers improved predictive performance of 0.4% on the Elixhauser-van Walraven and 6.1% on the Charlson weightings.Conclusions All weightings were validated with the national dataset and the new population-based weightings model improved the prediction of in-hospital mortality. The newly derive weights support patient population-based analysis of health outcomes.

Factors Affecting Treatment with Life-Saving Interventions, Computed Tomography Scans and Specialist Consultations

Background: Emergency treatments determined by emergency physicians may affect mortality and patient satisfaction. This paper attempts to examine the impact of patient characteristics, health status, the accredited level of hospitals, and triaged levels on the following emergency treatments: immediate life-saving interventions (LSIs), computed tomography (CT) scans, and specialist consultations (SCs). Methods: A multivariate logistic regression model was employed to analyze the impact of patient characteristics, including sex, age, income and the urbanization degree of the patient’s residence; patient health status, including records of hospitalization and the number of instances of ambulatory care in the previous year; the Charlson Comorbidity Index (CCI) score; the accredited level of hospitals; and the triaged level of emergency treatments. Results: All the patient characteristics were found to impact receiving LSI, CT and SC, except for income. Furthermore, a better health status was associated with a decreased probability of receiving LSI, CT and SC, but the number of instances of ambulatory care was not found to have a significant impact on receiving CT or SC. This study also found no evidence to support impact of CCI on SC. Hospitals with higher accredited levels were associated with a greater chance of patients receiving emergency treatments of LSI, CT and SC. A higher assigned severity (lower triaged level) led to an increased probability of receiving CT and SC. In terms of LSI, patients assigned to level 4 were found to have a lower chance of treatment than those assigned to level 5. Conclusions: This study found that several patient characteristics, patient health status, the accredited level of medical institutions and the triaged level, were associated with a higher likelihood of receiving emergency treatments. This study suggests that the inequality of medical resources among medical institutions with different accredited levels may yield a crowding-out effect.

Exploring how GPs discuss statin deprescribing with older people: a qualitative study

BackgroundGiven uncertainty surrounding benefits and harms, shifts in patient health status, and changing patient goals and preferences, statin deprescribing may be considered in some older people. This decision should be carefully discussed between GPs and patients.AimTo explore how GPs discuss deprescribing of statins with their older patients.Design & settingA qualitative study was undertaken using face-to-face, semi-structured interviews with Danish GPs from the regions of Southern Demark and Zealand.MethodThe GP participants belonged to group practices and were identified from personal networks and snowballing. The interviews lasted approximately 30 minutes and were conducted in English. They were analysed using systematic text condensation.ResultsA total of 11 GPs were interviewed and three themes were identified. (1) Reason for initiating a discussion: statin deprescribing mainly came up when GPs reviewed medication lists. There were differences between GPs regarding when or if they brought up deprescribing. (2) Discussion topics: GPs often discussed their interpretation of evidence surrounding statin use in older people. There were differences in how and if GPs discussed patient preferences. GPs viewed uncertainty and life expectancy as difficult to discuss. (3) Depth of discussion: the perceived level of patient engagement, and clinical context, could influence the extent of discussion.ConclusionGPs identified a range of topics that could be discussed with patients surrounding statin deprescribing. The depth and content of discussions varied according to the situation, and between GPs. Challenges may exist in communicating around certain topics, such as uncertainty and life expectancy. Further understanding of how to best communicate around challenging topics, and development of structured frameworks, may help facilitate statin deprescribing discussions. Identifying what patients think is important to discuss would provide necessary insight to promote quality discussions and shared understanding of the decision.