RESUMEN

Background: Reconstructing patient treatment trajectories is important to generate real-world evidence for epidemiological studies. The Danish National Patient Registry (DNPR) contains information about drug prescriptions and could therefore be used to reconstruct treatment trajectories. We aimed to evaluate and enhance two existing methods to reconstruct systemic anticancer treatment trajectories. Methods: This study was based on data from 8738 consecutive patients with solid tumors treated in the North Denmark Region between 2009 and 2019. Two approaches found in the literature as well as two new approaches were applied to the DNPR data. All methods relied on time intervals between two consecutive drug administrations to determine if they belonged to the same treatment line. MedOnc, a local dataset from the Department of Oncology, Aalborg University Hospital was used as a reference. To evaluate the performance of each method, F1-scores were calculated after matching the lines identified in both datasets. We used three different matching strategies: stringent matching, loose matching, and matching based on line numbers, controlling for overfitting. Results: Overall, the two new approaches outperformed the simpler and best performing of the two existing methods, with F1-scores of 0.47 and 0.45 vs 0.44 for stringent matching and 0.84 and 0.83 vs 0.82 for loose matching. Nevertheless, only one of the new methods outperformed the existing simpler method when matching on the number of lines (0.73 vs 0.72). Large differences were seen by cancer site, especially for the stringent and line number matchings. Performances were relatively stable by calendar year. Conclusion: The high F1-scores for the new methods confirm that they should be generally preferred to reconstruct systemic anticancer treatment trajectories using the DNPR.

RESUMEN

OBJECTIVES: Delays in treatment time intervals have been associated with overall survival in oral cavity squamous cell carcinoma (OCSCC). The aim of this study was to identify bottlenecks leading to prolonged treatment intervals. MATERIAL AND METHODS: A retrospective analysis was conducted using a cohort of OCSCC patients who underwent surgery and adjuvant radiation therapy. The endpoints of interest were prolonged treatment intervals. Multivariable logistic regression was used to adjust for patient and tumour characteristics. RESULTS: Median diagnosis-to-treatment interval (DTI) and surgery to initiation of postoperative radiation therapy interval (S-PORT) were 39 days (IQR 30-54) and 64 days (IQR 54-66), respectively. Prolonged DTI was associated with older age, worse Charlson Comorbidity index scores and worse T stages. Patients with prolonged DTI had longer times to preoperative imaging reports (25 vs 9 days; P < 0.01). Time to preoperative pathology did not differ. Prolonged S-PORT was associated with longer times to pathology report (28 vs 18 days; P < 0.01), to maxillofacial consult (38 vs 15 days; P < 0.01) and to maxillofacial approval of radiation (50 vs 28 days; P < 0.01). In patients requiring medical oncology consults, those with prolonged S-PORT had longer waiting times until consultation (58 vs 38 days; P = 0.02). Multivariate analysis showed independent predictors of prolonged DTI: time to preoperative imaging; and prolonged S-PORT: time to pathology report, time to maxillofacial consult, and time to medical oncology consult. CONCLUSIONS: Strategies targeting these organizational bottlenecks may be effective for shortening treatment time intervals, hence representing potential opportunities for improving oncological outcomes in OCSCC patients.

Asunto(s)

RESUMEN

Introduction: Administrative health data are increasingly used for disease surveillance, quality assurance and research purposes. In Austria, reporting of a standardized dataset is mandatory for each patient. Patients and methods: Routine documentation includes administrative and medical data, including admission and discharge characteristics, disease-diagnosis using ICD-10, medical procedure codes, and coding of involved hospital departments. Since 2015, a three-step pseudonymization on these data is provided including a pseudonym using secure hash algorithm 256, a non-recalculable record-ID, and age-groups of 5 years, allowing the reconstruction of individual patient-trajectories. We included persons aged ⩾20 years with an in-patient treatment in Austrian hospitals for acute stroke or transient ischemic attack (TIA) between 01.01.2015 and 31.12.2019 using medical record-linkage. Results: This totals 102,107 patients (49.3% women) with 107,055 treatment episodes. An ischemic stroke (IS) occurred in 60.9% (n = 65,133), 27.1% (n = 29,019) had a TIA, 3.3% (n = 3488) a subarachnoid hemorrhage, and 8.8% (n = 9415) an intracerebral hemorrhage (ICH). The study period covers 35.2 million person-years at risk, with a hospitalization rate for acute stroke of 221.8 per 100,000 person-years (95% CI 220.2-223.3), and 185.1 per 100,000 person-years (95% CI 183.7-186.5) for IS. Unscheduled re-admissions within 1 year occurred in 29.2% (95% CI 28.8-29.7) after IS, and 41.7% (95% CI 40.0-43.3) after ICH. Recurrent stroke occurred in 5.3% (95% CI 5.1-5.5) after IS, and 5.6% (95% CI 4.9-6.4) after ICH. Discussion: We present herein the details of a novel methodology to establish a nation-wide unselected Austrian stroke cohort, and to reconstruct pseudonymized individual longitudinal patient-trajectories on a national level. This approach shows potential applications in epidemiological research, quality assessment and outcome measurement. Conclusion: This novel approach opens new research fields, facilitates international comparison, and is needed for national benchmarking to assess the achievement of goals according to the Stroke Action Plan for Europe and augment the quality of Austria's integrated stroke care.

RESUMEN

BACKGROUND: Electronic Health Records (EHRs) aggregate diverse information at the patient level, holding a trajectory representative of the evolution of the patient health status throughout time. Although this information provides context and can be leveraged by physicians to monitor patient health and make more accurate prognoses/diagnoses, patient records can contain information from very long time spans, which combined with the rapid generation rate of medical data makes clinical decision making more complex. Patient trajectory modelling can assist by exploring existing information in a scalable manner, and can contribute in augmenting health care quality by fostering preventive medicine practices (e.g. earlier disease diagnosis). METHODS: We propose a solution to model patient trajectories that combines different types of information (e.g. clinical text, standard codes) and considers the temporal aspect of clinical data. This solution leverages two different architectures: one supporting flexible sets of input features, to convert patient admissions into dense representations; and a second exploring extracted admission representations in a recurrent-based architecture, where patient trajectories are processed in sub-sequences using a sliding window mechanism. RESULTS: The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression, using the publicly available Medical Information Mart for Intensive Care (MIMIC)-III clinical database. The results obtained demonstrate the potential of the first architecture to model readmission and diagnoses prediction using single patient admissions. While information from clinical text did not show the discriminative power observed in other existing works, this may be explained by the need to fine-tune the clinicalBERT model. Finally, we demonstrate the potential of the sequence-based architecture using a sliding window mechanism to represent the input data, attaining comparable performances to other existing solutions. CONCLUSION: Herein, we explored DL-based techniques to model patient trajectories and propose two flexible architectures that explore patient admissions on an individual and sequence basis. The combination of clinical text with other types of information led to positive results, which can be further improved by including a fine-tuned version of clinicalBERT in the architectures. The proposed solution can be publicly accessed at https://github.com/bioinformatics-ua/PatientTM.

Asunto(s)

RESUMEN

Objectives: Diagnosing patients with ANCA-associated vasculitis (AAV) can be challenging owing to its rarity and complexity. Diagnostic delay can have severe consequences, such as chronic organ damage or even death. Given that few studies have addressed diagnostic pathways to identify opportunities to improve, we performed a clinical audit to evaluate the diagnostic phase. Methods: This retrospective, observational study of electronic medical records data in hospitals focused on diagnostic procedures during the first assessment until diagnosis. Results: We included 230 AAV patients from nine hospitals. First assessments were mainly performed by a specialist in internal medicine (52%), pulmonology (14%), ENT (13%) or rheumatology (10%). The overall median time to diagnosis was 13 [interquartile range: 2-49] days, and in patients primarily examined by a specialist in internal medicine it was 6 [1-25] days, rheumatology 14 [4-45] days, pulmonology 15 [5-70] days and ENT 57 [16-176] days (P = 0.004). Twenty-two of 31 (71%) patients primarily assessed by a specialist in ENT had non-generalized disease, of whom 14 (64%) had ENT-limited activity. Two hundred and nineteen biopsies were performed in 187 patients (81%). Histopathological support for AAV was observed in 86% of kidney biopsies, 64% of lung biopsies and 34% of ENT biopsies. Conclusion: In The Netherlands, AAV is diagnosed and managed predominantly by internal medicine specialists. Diagnostic delay was associated with non-generalized disease and ENT involvement at presentation. Additionally, ENT biopsies had a low diagnostic yield, in contrast to kidney and lung biopsies. Awareness of this should lead to more frequent consideration of AAV and early referral for a multidisciplinary approach when AAV is suspected.

RESUMEN

OBJECTIVE: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. MATERIALS AND METHODS: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression. RESULTS: The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. DISCUSSION: Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. CONCLUSION: Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

Asunto(s)

RESUMEN

Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread. To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients' trajectory data. By using innovative space-time analytics, this framework reveals the spatiotemporal patterns of patients' mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.

RESUMEN

Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.

Asunto(s)

RESUMEN

As more data is generated from medical attendances and as Artificial Neural Networks gain momentum in research and industry, computer-aided medical prognosis has become a promising technology. A common approach to perform automated prognoses relies on textual clinical notes extracted from Electronic Health Records (EHRs). Data from EHRs are fed to neural networks that produce a set with the most probable medical problems to which a patient is subject in her/his clinical future, including clinical conditions, mortality, and readmission. Following this research line, we introduce a methodology that takes advantage of the unstructured text found in clinical notes by applying preprocessing, concepts extraction, and fine-tuned neural networks to predict the most probable medical problems to follow in a patient's clinical trajectory. Different from former works that focus on word embeddings and raw sets of extracted concepts, we generate a refined set of Unified Medical Language System (UMLS) concepts by applying a similarity threshold filter and a list of acceptable concept types. In our prediction experiments, our method demonstrated AUC-ROC performance of 0.91 for diagnosis codes, 0.93 for mortality, and 0.72 for readmission, determining an efficacy that rivals state-of-the-art works. Our findings contribute to the development of automated prognosis systems in hospitals where text is the main source of clinical history.

RESUMEN

Objectives: In this study, we explored how ovarian cancer (OC) survivors give meaning to their cancer experience and how the latter has an impact on their quality of life (QOL).Participants: The sample comprised 16 OC patients participating in the French study Vivrovaire in Lyon who were in long-term remission.Methods: We employed a qualitative approach, based on semi-structured interviews. Using ATLAS.ti software, we performed a thematic analysis of the collected data.Findings: Three main OC-related themes emerged: body and physical issues; social life evolutions; participant retrospective perception of OC experience.Interpretation: Our results underline the need to take into account the various dimensions of patient identity when studying OC survivors' QOL and to consider intra-individual QOL evolutions from a temporal perspective.Implications for Psychosocial Providers: Helping patients acquire a sound understanding of their illness experience is an enormous challenge for OC healthcare.

Asunto(s)

RESUMEN

AIMS AND OBJECTIVES: To explore home care nurses' experiences of implementation and use of checklists developed for improving continuity of care for older patients (65+ years). BACKGROUND: The Norwegian Coordination Reform was implemented to improve coordination between hospitals and communities and facilitate a quicker return to home community after hospital discharge. To follow-up, national learning networks were initialised to improve pathways for chronically ill older patients, including the development and use of standardised checklists. DESIGN: An explorative qualitative design was chosen. METHODS: Three focus group interviews were conducted, including 18 registered nurses from eight municipalities in southern Norway. Systematic text condensation was used to analyse the interview texts. The COREQ checklist was followed. RESULTS: Three categories emerged from the analysis. (a) "The implementation process" included the experiences of a chaotic beginning, the importance of involvement, the leaders' role and resource allocation. (b) "Pros and cons of checklists in use" included the informants' experiences of checklists' usefulness for nurses and the patients. (c) "Competence needed" included the need for a comprehensive set of formal, experiential and social competences. CONCLUSION AND RELEVANCE TO CLINICAL PRACTICE: The leaders' role, support and engagement are decisive for a successful implementation. To succeed and establish solid routines, allocating resources when implementing new laborious routines, such as checklists, is important. To improve holistic continuity of care to chronically ill older patients, checklists should be customisable to each patient's needs, be comprehensive enough to grasp the essence in what to be done at several time points, but at the same time brief enough to be operational. Checklists can be a useful tool for home care nurses, if customised to the individual municipality and the staffs' working routines. It is important that the staff have versatile and extensive competencies enabling them to use the checklists appropriately.

Asunto(s)

RESUMEN

OBJECTIVE: Timely treatment of patients with early RA (ERA) favours a beneficial disease outcome. However, individuals often delay their contact with a health-care professional (HCP) after ERA-related symptom onset. The aim of this study was to investigate the perspective of patients on the journey of a patient from RA symptom onset until referral to a specialist. METHODS: A subgroup of patients with ERA from the Care in ERA (CareRA) trial were interviewed retrospectively to discuss their initial ERA-related experiences preceding diagnosis, using a bespoke assessment form. The first section of the form focused on initial symptoms and help-seeking behaviour by the patients. The second part probed the actions of the HCPs consulted. Additional notes derived from the patient stories were analysed thematically. RESULTS: Among 94 patients, pain (97%), swelling (73%) and stiffness (52%), typically in multiple joints, were reported as initial ERA symptoms. The general practitioner (GP) was generally the first HCP to be contacted (87%). Frequently reported reasons to visit an HCP were intense pain (90.4%) and difficulties in performing daily activities (69%). In 44.1% of patients, the HCP suspected ERA at the first visit. Approximately 25% of patients needed more than five visits before detection of ERA. GPs mainly referred patients to rheumatologists (71%). Thematic analysis uncovered that multiple HCPs were often involved in the journey to RA detection and referral. CONCLUSION: Pain is the most commonly reported initial symptom of ERA and the main reason to visit an HCP, usually a GP. These GPs play a pivotal role in early detection and correct referral. Furthermore, the journey of a patient seems complex, often with multiple HCPs being involved.

RESUMEN

INTRODUCTION: Formal pathways models outline that patients should receive information in order to experience a coherent journey but do not describe an active role for patients or their relatives. The aim of this is paper is to articulate and discuss the active role of patients during their cancer trajectories. METHODS AND THEORY: An in-depth case study of patient trajectories at a Danish hospital and surrounding municipality using individual interviews with patients. Theory about trajectory and work by Strauss was included. RESULTS: Patients continuously took initiatives to organize their treatment and care. They initiated processes in the trajectories, and acquired information, which they used to form their trajectories. Patients presented problems to the healthcare professionals in order to get proper help when needed. DISCUSSION: Work done by patients was invisible and not perceived as work. The patients' requests were not sufficiently supported in the professional organisation of work or formal planning. Patients' insertion and use of information in their trajectories challenged professional views and working processes. And the design of the formal pathway models limits the patients' active participation. When looking at integrated care from the perspective of patients, the development of a more holistic and personalized approach is needed.