Surgical Management of Gastroesophageal Reflux in Children: Risk Stratification and Prediction of Outcomes

Introduction: Since the 1980s fundoplication, an operation developed for adults with hiatus hernia and reflux symptoms, has been performed in children with gastroesophageal reflux disease (GORD). When compared to adult outcomes, paediatric fundoplication has resulted in higher failure and revision rates. In the first chapter we explore differences in paradigm, patient population and outcomes. Firstly, symptoms are poorly defined and are measured by instruments of varying quality. Secondly, neurological impairment (NI), prematurity and congenital anomalies (oesophageal atresia, congenital diaphragmatic hernia) are prevalent in children. / Purpose: To develop methods for stratifying paediatric fundoplication risk and predicting outcomes based on symptom profile, demographic factors, congenital and medical history. / Methods: Study objectives are addressed in three opera: a symptom questionnaire development (TARDIS:REFLUX), a randomised controlled trial (RCT) and a retrospective database study (RDS). TARDIS: REFLUX: In the second chapter, digital research methods are used to design and validate a symptom questionnaire for paediatric GORD. The questionnaire is a market-viable smartphone app hosted on a commercial platform and trialed in a clinical pilot study. / RCT: In the third chapter, the REMOS trial is reported. The trial addresses the subset of children with NI and feeding difficulties. Participants are randomized to gastrostomy with or without fundoplication. Notably, pre- and post-operative reflux is quantified using pH-impedance. / RDS: In the fourth chapter, data mining and machine learning strategies are applied to a retrospective paediatric GORD database. Predictive modelling techniques applied include logistic regression, decision trees, random forests and market basket analysis. / Results and conclusion: This work makes two key contributions. Firstly, an effective methodology for development of digital research tools is presented here. Secondly, a synthesis is made of literature, the randomised controlled trial and retrospective database modelling. The resulting product is an evidence-based algorithm for the surgical management of children with GORD

Heart failure – risk factors and the validity of diagnoses

Heart failure (HF) is a global health problem. HF risk factors remain understudied. The roles that diabetes and sodium consumption play in HF remain unknown. Furthermore, the validity of HF diagnoses in the Finnish Hospital Discharge Register (FHDR) has not been thoroughly evaluated. This thesis aims to discover sodiumand diabetes-related HF risk factors, validate FHDR-based HF diagnoses, and investigate if the subtyping of register-based HF diagnoses could be improved through electronic health record (EHR) data mining. A 24-hour urinary sodium excretion (mean 183 mmol/d) was measured from 4,630 individuals to assess the relationship between salt intake and incident HF (Study I). We used data from 3,834 diabetic and 90,177 nondiabetic individuals to evaluate the diabetes status-related differences in risk factors and mediators of HF (Study II). Medical records of 120 HF cases and 120 controls were examined to study the validity of HF diagnoses (Study III). We drew data from 33,983 patients to assess if HF diagnoses could be subtyped more accurately through EHR data mining (Study IV) and validated the mining-based versus clinical subtyping in 100 randomly selected patients. In Study I, we observed that high sodium intake was associated with incident coronary artery disease (CAD) and diabetes, but not HF. In Study II, the risk of HF was 2.7-fold in individuals with diabetes compared to nondiabetic participants. Conventional cardiovascular disease risk factors and biomarkers for cardiac strain, myocardial injury, and inflammation were associated with incident HF in both groups. The strongest mediators of HF in diabetes were the direct effect of diabetes and the indirect effects mediated by obesity, cardiac strain/volume overload, and hyperglycemia. In studies III and IV, HF diagnoses of the FHDR had good predictive values (NPV 0.83, PPV 0.85), even when patients with preexisting heart conditions were used as controls. With additional EHR-mined data, the accuracy of our algorithm to correctly classify individuals into HF subtypes versus clinical assessment was 86 %. The findings in this thesis show that register-based HF is an accurate endpoint and that EHR data mining can improve this accuracy. Our results also elucidate the role of sodium and diabetes as HF risk factors.Sydämen vajaatoiminta: riskitekijät ja diagnoosien validiteetti Sydämen vajaatoiminta on maailmanlaajuinen terveysongelma, jonka riskitekijät ovat osin epäselviä. Suolan käytön yhteyttä ja diabeteksen aiheuttamaa korkeaa riskiä vajaatoimintaan ei ole riittävästi tutkittu. Vajaatoimintadiagnoosien validiteettia Hoitoilmoitusjärjestelmä (HILMO)-sairaalarekisterissä ei tiedetä. Tässä väitöskirjatyössä tutkittiin suolaan ja diabetekseen liittyviä sydämen vajaatoiminnan riskitekijöitä, validoitiin HILMO-pohjaiset vajaatoimintadiagnoosit ja selvitettiin, voidaanko vajaatoimintaa alatyypittää tekstinlouhintaa käyttämällä. Suolan saannin ja vajaatoiminnan välisen suhteen arvioimiseksi (tutkimus I) tutkittiin 4 630 henkilön vuorokausivirtsan natrium (keskimäärin 183 mmol/d). Diabetekseen liittyvien sydämen vajaatoiminnan riskitekijöiden selvittämiseksi (tutkimus II) käytiin läpi 3 834 diabeetikon ja 90 177 verrokin tiedot. Vajaatoimintadiagnoosien validiteettia (tutkimus III) varten tutkimme 120 vajaatoimintatapauksen ja 120 verrokin (joilla oli muu sydänsairaus) potilastiedot ja tarkempaa alatyypitystä (tutkimus IV) varten keräsimme tietoja 33 983 potilaasta ja validoimme tiedonlouhintaan perustuvan alatyypityksen 100 satunnaisella potilaalla. Tutkimuksessa I suolan saanti oli yhteydessä sepelvaltimotaudin ja diabeteksen kehittymiseen, mutta tulokset eivät olleet merkitseviä vajaatoiminnan osalta. Tutkimuksessa II diabeetikoiden vajaatoimintariski oli 2,7-kertainen verrokkeihin verrattuna. Molemmilla tavanomaiset riskitekijät ja sydämen venyvyyden, sydänvaurion ja tulehduksen merkkiaineet olivat yhteydessä vajaatoimintaan. Merkittävimmät diabeteksen vajaatoimintaa välittävät muuttujat olivat diabeteksen suora vaikutus sekä epäsuorat ylipainon, sydämen venymisen ja hyperglykemian vaikutukset. Tutkimuksissa III ja IV HILMO-rekisterin vajaatoimintadiagnoosin prediktiiviset arvot olivat hyviä (NPV 0,83, PPV 0,85) verrattuna muihin sydänsairaisiin potilaihin ja tiedonlouhinnan alatyypityksen tarkkuus verrattuna kliiniseen oli 86 %. Tämä väitöskirja osoittaa, että HILMO-pohjaiset vajaatoimintadiagnoosit toimivat tieteellisenä päätetapahtumana ja että vajaatoiminnan alatyyppiä voidaan tarkentaa tekstilouhinnalla, sekä tuo uutta tietoa suolasta ja diabeteksesta vajaatoiminnan riskitekijöinä

Doctor of Philosophy

dissertationWith the growing national dissemination of the electronic health record (EHR), there are expectations that the public will benefit from biomedical research and discovery enabled by electronic health data. Clinical data are needed for many diseases and conditions to meet the demands of rapidly advancing genomic and proteomic research. Many biomedical research advancements require rapid access to clinical data as well as broad population coverage. A fundamental issue in the secondary use of clinical data for scientific research is the identification of study cohorts of individuals with a disease or medical condition of interest. The problem addressed in this work is the need for generalized, efficient methods to identify cohorts in the EHR for use in biomedical research. To approach this problem, an associative classification framework was designed with the goal of accurate and rapid identification of cases for biomedical research: (1) a set of exemplars for a given medical condition are presented to the framework, (2) a predictive rule set comprised of EHR attributes is generated by the framework, and (3) the rule set is applied to the EHR to identify additional patients that may have the specified condition. iv Based on this functionality, the approach was termed the ‘cohort amplification' framework. The development and evaluation of the cohort amplification framework are the subject of this dissertation. An overview of the framework design is presented. Improvements to some standard associative classification methods are described and validated. A qualitative evaluation of predictive rules to identify diabetes cases and a study of the accuracy of identification of asthma cases in the EHR using frameworkgenerated prediction rules are reported. The framework demonstrated accurate and reliable rules to identify diabetes and asthma cases in the EHR and contributed to methods for identification of biomedical research cohorts

Predicting Intensive Care Unit Length of Stay via Supervised Learning

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Laura Igual Muñoz[en] Healthcare is a traditional sector that is demanding, nowadays, a profound change regarding tasks and ways of work. The explotation of data-based analytical techniques together with computational capabilities are potential candidates to lead part of that demanding change. This can cause an innovation to the sector with considerable social impact. In any case, it is necessary to take into account the specific characteristics of the clinical data: quality, volume, access and multimodality. In this Master Thesis, an analysis of the data from critical patients was carried out in order to study the influence of several observables to determine their Length of Stay in the Intensive Care Unit. Try to solve that problem can help a lot not only the physicians from the mere investigation purposes point of view but also the healthcare sector because Intensive Care Unit logistics counts and it can become very important

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Non-conveyance and patient safety in prehospital emergency care

Emergency medical services (EMS) and emergency departments (EDs) have reported increased attendance rates and numbers of patients without urgent need for treatment. Because of controversy about the unnecessary conveyance to the ED, EMS has increasingly discharged patients at the scene, although it is unclear how non-conveyance ensures patient safety. This was a prospective cohort study with three sub-studies from three regions in Finland. The overall aim was to explore whether EMS non-conveyance ensures patient safety. EMS re-contact, unscheduled visits to a primary health care facility or ED, and hospitalization within 0–24 and 24–48 h were the primary outcomes, and mortality in 28 days was the secondary outcome. Multivariable logistic regressions analyses, machine learning in the form of text classification, and manual analyses were used to identify predictors of adverse events. The study data comprise 40,263 EMS patients, 42% of whom were discharged at the scene. Among the included non-conveyed patients (n=11,861), 6.3% recontacted EMS, 8.3% visited a primary health care facility and 4.2% the ED, 1.6% were admitted to the hospital, 0.3% were treated in intensive care, and 0.1% died within 0–24 h after the non-conveyance. These rates were lower within 24–48 h than within 0–24 h. Factors associated with non-conveyance and a subsequent primary health care visit were non-urgent mission priority, involvement of an advanced life support unit (ALS), EMS arrival at night, and rural location. FastText-model (area under the curve (AUC), 0.654) and manual analyses indicated that several health care re-contacts were planned before between the patient and EMS personnel. In conclusion, most patients did not have events after EMS non-conveyance. Post-non-conveyance re-contacts do not necessarily indicate that patient safety was jeopardized, as these contacts can represent previously planned visits to health care facilities. Non-conveyance by EMS does not appear to compromise patient safety, but further studies are warranted.Kuljettamatta jättämisen potilasturvallisuus ensihoidossa Ensihoitopalvelun ja päivystysten suuret potilasmäärät ja turhat päivystyskäynnit on tunnistettu laajasti. Ensihoitajat jättävät paljon potilaita kuljettamatta jatkohoitoon, jos välitöntä päivystyksellistä hoidon tarvetta ei ole. Menettelyn potilasturvallisuus on kuitenkin epäselvä. Tämän prospektiivisen kohorttitutkimuksen aineisto kerättiin kolmen sairaanhoitopiirin alueelta Suomessa. Tutkimuskokonaisuuden tarkoituksena oli selvittää, onko ensihoitajien tekemä päätös potilaan kuljettamatta jättämisestä potilasturvallista. Ensisijaisia päätetapahtumia olivat uusi ensihoitotehtävä, päivystyskäynti perusterveydenhuollossa tai erikoissairaanhoidossa, sekä sisäänotto sairaalaan 0–24 ja 24–48 h kuljettamatta jättämisen jälkeen. Potilaan kuolema 28 vuorokauden aikana oli toissijainen päätetapahtuma. Logistista regressioanalyysiä (monimuuttujamalli), tekstin louhintaa ja koneoppimista, sekä manuaali- ja sisällön analyyseja käytettiin haittatapahtumia ennustavien tekijöiden tunnistamisessa. Ensihoidon potilaista (n=40,263) 42 % jätettiin kuljettamatta jatkohoitoon. Mukaan otetuista potilaista (n=11,861) 6.3 % oli uusi ensihoitotehtävä, 8.3 % käynti perusterveydenhuollossa, 4.2 % käynti erikoissairaanhoidossa, 1.6 % otettiin sisälle sairaalaan, 0.3 % hoidettiin teho-osastolla ja 0.1 % menehtyi 0–24 h. Vastaavat prosentit olivat matalampia 24–48 h kuljettamatta jättämisen jälkeen. Kiireetön ensihoitotehtävä, hoitotason yksikkö, ilta-yöaika ja haja-asutusalue lisäsivät perusterveydenhuollon käynnin todennäköisyyttä. FastText-mallin (AUC 0.654) ja manuaalisten analyysien mukaan moni päätetapahtumista oli suunniteltu etukäteen. Suurimmalla osalla potilaista ei ollut päätetapahtumia seurantajakson aikana ja terveydenhuollon kontakteista moni oli suunniteltu etukäteen. Ensihoitajien tekemä hoidon tarpeen arvio ja päätös kuljettamatta jättämisestä näyttää olevan potilasturvallista, mutta lisätutkimuksia tarvitaan

Proceedings - Wright State University Boonshoft School of Medicine Eighth Annual Medical Student Research Symposium: Celebrating Medical Student Scholarship

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Eighth Annual Boonshoft School of Medicine Medical Student Research Symposium held on April 13, 2016.https://corescholar.libraries.wright.edu/ra_symp/1007/thumbnail.jp

Nurse Escorts’ Perceptions of Their Ability to Manage Patient Clinical Deterioration During Nurse-Led Inter-Hospital Ambulance Transfer in the Wheatbelt Region of Western Australia: A Mixed Methods Study

The Western Australia (WA) Country Health Service (WACHS) requires a ward or emergency department registered nurse (RN) to assume the responsibility of conducting inter-hospital nurse-led patient ambulance transfers. In WACHS, these nurses are usually generalist nurses with no specialised training. WACHS has various escalation policies, guidelines and support systems for nurses when they are located within the hospital and wards. However, despite these escalation protocols being clear in this setting, their relevance and practicality during patient transport is uncertain. This research explores how well equipped WACHS RNs in the Wheatbelt region of WA are in managing clinical deterioration of patients during inter-hospital nurse-led ambulance transfers. The WACHS Wheatbelt has identified ‘failure to recognise the need to escalate clinical care’ as a clinical risk in the in-hospital setting. The risk outlines knowledge and skills deficits, lack of access to specialist advice, failure to recognise observations that fall into the parameters that require intervention, and failure by nurses to follow clinical deterioration policy as causes that result in treatment delay, increased morbidity and mortality, delay in transfer, and increased length of stay. It should be appreciated that during road transfer there are additional factors that will increase the risk of failure to adequately detect and manage acute clinical deterioration. This study aims to • explore nurses’ perceptions about caring for a patient during road ambulance transfer, acknowledgement of clinical deterioration, and its occurrence on patients being transferred, and how well equipped the nurse escort is in detecting and managing acute deterioration; and • seek to support future policy formulation and decision-making with regard to nurses training, induction and ongoing education on inter-hospital transfer. This study employed a mixed methods descriptive design using quantitative and qualitative data obtained in two phases. In Phase One using an online survey, the study explored the self-reported skills level of the RNs, the support available during transport, their perceptions of their role and abilities during transport, and their confidence and knowledge to enact policies that govern their practice away from the hospital setting. In Phase Two, the nursing leaders and policy makers were interviewed on an individual face-to-face basis, where they were requested to clarify, elaborate or comment on the quantitative and qualitative data from Phase One. Phase One respondents acknowleged that nursing a patient in an ambulance had associated risks that require advanced clinical skills and confidence that would not normally be as critical when working within a hospital and with a team. Ambulance transfer logistics and inherent challenges require a trained patient escort. Respondents highlighted different practices, use of different guiding tools, and processes that were not uniformly applied within the region. This variation was evidenced in the different documentation kept by nurses during transfer, different interpretation of available policies, escalation processes for deterioration, and general attitude towards conducting these transfers. Inter-hospital patient transfers were viewed as complicated with associated risks, most of which were expected and cannot be completely eliminated. However, there was an acknowledgement that some of the factors that negatively affect these transfers could be eliminated by clearer guidelines and support for the transferring nurse. During Phase Two, a significant finding highlighted how the patient was in most instances safe, but the likely lack of support for the nurses due to ambiguity with inadequate backup was reaffirmed. Phase Two also confirmed that if strategies were to be put in place to guide, support and prioritise not only patient safety but also nurses’ welfare, then the model of using RNs to conduct inter-hospital nurse-led patient transfers would need to be sustainable and can be improved. This was important to note as it is unlikely that the RN will remain the most likely staff member to continue to meet the ever-growing demand to transfer patients intra-regionally and to metropolitan areas by road ambulance. There was a general appreciation that inter-hospital transfers are complex and that the WA rural health setting is unique and challenging. The generalist RN was viewed as having vital transferrable skills to adequately care for patients being transferred. These RNs were reported to be skilful and resilient in a setting where there is limited support for their personal wellbeing or professional development. The policies relating to inter-hospital patient transfers were assessed as unfamiliar, irrelevant or impractical, leading to disparities between what the policy stipulates and the realities of practice. This study will be critical in supporting health service discussions about policy formation and decision-making with regard to nurses’ training, induction, ongoing education and support in the ever-growing nurses’ responsibility of transferring patients between hospitals

User Interfaces for Patient-Centered Communication of Health Status and Care Progress

The recent trend toward patients participating in their own healthcare has opened up numerous opportunities for computing research. This dissertation focuses on how technology can foster this participation, through user interfaces to effectively communicate personal health status and care progress to hospital patients. I first characterize the design space for electronic information communication to patients through field studies conducted in multiple hospital settings. These studies utilize a combination of survey instruments, and low- and high-fidelity prototypes, including a document-editing prototype through which users can view and manage clinical data to automatically associate it with progress notes. The prototype, activeNotes, includes the first known techniques supporting clinical information requests directly within a document editor. A usage study with ICU physicians at New York-Presbyterian Hospital (NYP) substantiated our design and revealed how electronic information related to patient status and care progress is derived from a typical Electronic Health Record system. Insights gained from this study informed following studies to understand how to design abstracted, plain-language views suitable for patients. We gauged both patient and physician responses to information display prototypes deployed in patient rooms for a formative study exploring their design. Following my reports on this study, I discuss the design, development and pilot evaluations of a prototype Personal Health Record application providing live, abstracted clinical information for patients at NYP. The portal, evaluated by cardiothoracic surgery patients, is the first of its kind to allow patients to capture and monitor live data related to their care. Patient use of the portal influenced the subsequent design of tools to support users in making sense of online medication information. These tools, designed with nurses and pharmacists and evaluated by cardiothoracic surgery patients at NYP, were developed using topic modeling approaches and text analysis techniques. Embodied in a prototype called Remedy, they enable rapid filtering and comparison of medication-related search results, based on a number of website features and content topics. I conclude by discussing how findings from this series of studies can help shape the ongoing design and development of patient-centered technology