title stringlengths 1 1.19k | keywords stringlengths 0 668 | concept stringlengths 0 909 | paragraph stringlengths 0 61.8k | PMID stringlengths 10 11 |
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CONFLICT OF INTEREST | ONCOLOGY | YKK has served as a consultant for ALX Oncology, Zymeworks, Amgen, Novartis, Macrogenics, Daehwa, Blueprint, Surface Oncology, BMS, and Merck (MSD). MHR received honoraria from DAEHWA Pharmaceutical, Bristol Myers Squibb, Lilly, Ono Pharmaceutical, MSD, Taiho Pharmaceutical, Novartis, Daiichi Sankyo, and AstraZeneca, a... | PMC10134272 | |
ETHICS APPROVAL AND CONSENT TO PARTICIPATE | This study was approved by the institutional review boards of each participating institution, and it was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. All participants provided written informed consent before enrollment. This study is registered at | PMC10134272 | ||
CONSENT FOR PUBLICATION | Personal patient data are not included in this manuscript. All authors confirm their consent for this article to be published. | PMC10134272 | ||
Supporting information |
Figure S1.
Figure S2.
Figure S3.
Figure S4.
Click here for additional data file. | PMC10134272 | ||
ACKNOWLEDGMENTS | The authors thank all of the patients who participated in this study, and all of the investigators in Korea, China, and Taiwan. This study is an investigator‐initiated trial and Bayer supported this study by providing the study drugs and financing the study evaluation and the central radiological review. However, Bayer... | PMC10134272 | ||
DATA AVAILABILITY STATEMENT | Data sharing is not applicable to this article as no new data were created or analyzed in this study. | PMC10134272 | ||
REFERENCES | PMC10134272 | |||
Background: | Oxygen uptake (VO | PMC10198721 | ||
Methods: | Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (n | PMC10198721 | ||
Results: | Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m | PMC10198721 | ||
Conclusions: | Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO | PMC10198721 | ||
Funding: | No external funding was received for this work. | PMC10198721 | ||
Research organism | PMC10198721 | |||
Introduction | The oxygen uptake (VOVOHowever, VOVOFor many years researchers have studied indirect methods of estimating VORecently, we have been observing the development of prediction methods with the usage of machine learning (ML) and artificial intelligence (AI) (Therefore, in this research, with the support of ML, we look for a... | PMC10198721 | ||
Materials and methods | We have applied the development and validation of the prediction TRIPOD guidelines to conduct the study (see Supplementary Material 1TRIPOD Checklist for Prediction Model Development and Validation) ( | PMC10198721 | ||
Ethical approval | AKBE/32/2021 | The Institutional Review Board of the Bioethical Committee at the Medical University of Warsaw (AKBE/32/2021) has approved the study protocol. The regulations of the Declaration of Helsinki were met during all parts of the study. Each study participant delivered written consent to undergo CPET and participate in the st... | PMC10198721 | |
Derivation cohort | REGRESSION | We selected the cohort with the use of rigorous exclusion/inclusion criteria. Due to the insufficient number of women in our database and the number of potential variables in the regression models for adequate power, we had to limit ourselves to conduct analysis in the male population only (Participants’ selection proc... | PMC10198721 | |
Flowchart of the preliminary inclusion and exclusion process. | Abbreviations: EA, endurance athlete; CPET, cardiopulmonary exercise testing; SD, standard deviation; TE, treadmill; RER, respiratory exchange ratio; VO | PMC10198721 | ||
Somatic measurements and CPET protocols | Body mass was measured with a body composition (BC) analyser (Tanita, MC 718, Japan) with the multifrequency of 5 kHz/50 kHz/250 kHz via the bioimpedance analysis and normal testing mode. The participants’ skin was cleaned with alcohol before placing the electrodes on the skin. Prior to the test, the participants recei... | PMC10198721 | ||
Data analysis | Our comprehensive ML approach enables the evaluation of each formula by preliminary variables precision (at the stage of selection), then accuracy (during the model’s building) and recall (in internal validation).Individual CPET results were saved into the Excel file (Microsoft Corporation, Redmond, WA, USA) and a cust... | PMC10198721 | ||
Basic anthropometric characteristics for runners. | BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. | PMC10198721 | ||
Basic anthropometric characteristics for cyclists. | BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. | PMC10198721 | ||
Cardiopulmonary exercise testing (CPET) characteristics for runners. | CI, 95% confidence interval; SD, standard deviation; rVO | PMC10198721 | ||
Cardiopulmonary exercise testing (CPET) characteristics for cyclists. | REGRESSION | CI, 95% confidence interval; SD, standard deviation; rVOAfter selection variables were included in the further analysis, only selected parameters were put into multiple linear regression (MLR) modelling. The data for MLR model building were randomly distributed into sets, that is derivation, testing, validation represe... | PMC10198721 | |
Results | PMC10198721 | |||
Somatic measurements and CPET results | Anthropometric data of the runners models for derivation, testing, and validation groups are presented in CPET results for runners models are presented in | PMC10198721 | ||
Prediction models based on AT and RCP | Full forms of MLR prediction models for cyclists are demonstrated in | PMC10198721 | ||
Performance of prediction equations for VO | Abbreviations: VO | PMC10198721 | ||
VO | RESPIRATORY COMPENSATION | AT, equation based on anaerobic threshold; RCP, equation based on respiratory compensation point; SOM, equation based on somatic variables only; R | PMC10198721 | |
VO | RESPIRATORY COMPENSATION | AT, equation based on anaerobic threshold; RCP, equation based on respiratory compensation point; SOM, equation based on somatic variables only; R | PMC10198721 | |
Models validation | Evaluation of each model for cyclists is presented in | PMC10198721 | ||
Bland-Altman plots comparing observed with predicted VO | Abbreviations: VO | PMC10198721 | ||
Discussion | In the present study, we derived and internally validated novel advanced and accurate prediction models for VOThe main advantage of our research is the unified CPET protocol conducted on a wide cohort of endurance athletes with different levels of fitness. This approach enables the comprehensive evaluation of the most ... | PMC10198721 | ||
Conclusion | Briefly, we provided new prediction models for VO | PMC10198721 | ||
Additional information | PMC10198721 | |||
Competing interests | received payment for leading CPET workshops at IX Małopolskich Warsztatach Niewydolności Serca. The author has no other competing interest to declare.No competing interests declared.has received funding from the Institute of Sport - National Research Institute. The author has received consulting fees for regular coachi... | PMC10198721 | ||
Author contributions | Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing.Data curation, Writing – original draft.Data curation, Writing – original draft.Data curation, Methodology, Writing – origin... | PMC10198721 | ||
Ethics | AKBE/32/2021 | Human subjects: The Institutional Review Board of the Bioethical Committee at the Medical University of Warsaw (AKBE/32/2021) has approved the study protocol. The regulations of the Declaration of Helsinki were met during all parts of the study. Each subject delivered written consent to undergo CPET and participate in ... | PMC10198721 | |
Additional files | PMC10198721 | |||
Source code in Python for transforming files in the database. | PMC10198721 | |||
TRIPOD checklist. | PMC10198721 | |||
Data availability | All data generated or analysed during this study are included in the manuscript. | PMC10198721 | ||
References | The authors have established new formulas to predict maximum oxygen uptake for cyclists and runners based on submaximal exercise testing and anthropometric characteristics. This is an important study with a large and comprehensive dataset, which may be helpful for many exercise labs. The work is convincing, using appro... | PMC10198721 | ||
Background | pain | these authors contributed equallyPostoperative dental pain is pervasive and can affect a patient’s quality of life. Adopting a patient-centric approach to pain management involves having contemporaneous information about the patient’s experience of pain and using it to personalize care. | PMC10644946 | |
Objective | pain | In this study, we evaluated the use of a mobile health (mHealth) platform to collect pain-related patient-reported outcomes over 7 days after the patients underwent pain-inducing dental procedures; we then relayed the information to the dentist and determined its impact on the patient’s pain experience. | PMC10644946 | |
Methods | pain | The study used a cluster-randomized experimental study design with an intervention arm where patients were prompted to complete a series of questions relating to their pain experience after receiving automated text notifications on their smartphone on days 1, 3, 5, and 7, with the resulting information fed back to dent... | PMC10644946 | |
Results | pain | SECONDARY | A total of 42 providers and 1525 patients participated. For the primary outcome (pain intensity on a 1 to 10 scale, with 10 being the most painful), intervention group patients reported an average pain intensity of 4.8 (SD 2.6), while those in the control group reported an average pain intensity of 4.7 (SD 2.8). These ... | PMC10644946 |
Conclusions | pain | POSTOPERATIVE COMPLICATION | While the mHealth platform did not have a significant impact on acute postoperative pain experience, patients and providers indicated improvement in patient-provider communication, patient-provider relationship, postoperative complication management, and ability to manage pain medication prescribing. Expanded collabora... | PMC10644946 |
Trial Registration | ClinicalTrials.gov NCT03881891; | PMC10644946 | ||
Introduction | postoperative pain, pain | The experience of pain is a national and global public health problem with significant physical, cognitive, and emotional costs [While dentists are prescribing fewer postoperative opioids [Adopting a patient-centric approach to pain management involves collecting valuable information about the patient’s experience of p... | PMC10644946 | |
Methods | PMC10644946 | |||
Study Overview | postoperative pain, pain | A 24-month phase 2 cluster randomized controlled trial was conducted to evaluate the impact of using an mHealth platform on patient postoperative pain experiences, satisfaction with pain management, and dental provider satisfaction with the platform. The multicenter study was conducted at an academic dental institution... | PMC10644946 | |
Study Sites and Participants | The study was conducted at two dental institutions. One is part of an academic dental site and the other is a large privately held dental group practice of around 50 offices across the Pacific Northwest region of the United States. The academic dental center comprises predoctoral, resident, and faculty clinics. The pat... | PMC10644946 | ||
Included “Pain-Inducing” Procedures | D7311 | The core set of pain-associated dental procedure codes (Code to Dental Terminology; American Dental Association) included were endodontics: D3310, D3320, D3330, D3346, D3347, D3348, D3410, D3421, D3425, D3426, and D3450; periodontal surgery: D4210, D4211, D4212, D4240, D4241, D4249, D4260, D4261, and D4263; oral surger... | PMC10644946 | |
Intervention | pain | The mHealth platform deployed in this study was FollowApp.Care. A detailed description of the platform has been previously published [On completion of any of the eligible procedures, enrolled patients (including intervention and control groups) received postoperative care instructions and guidance according to each ins... | PMC10644946 | |
Randomization | Each of the participating providers was randomized to one study arm (the mHealth intervention plus standard care vs standard care only), and each patient automatically assumed the randomization status of their provider. As such, each patient was nested within a specific provider (Randomization scheme: each provider rep... | PMC10644946 | ||
Means of Data Collection | pain | We used the mHealth platform (FollowApp.Care) to collect PRO data (pain experience) from patients after dental procedures. Electronic health record (EHR) data for postprocedure prescribing data was extracted using the patient enrollment data. EHR data was then merged with the mHealth survey response data for each patie... | PMC10644946 | |
Study Outcomes | pain | The primary PRO of interest was pain intensity—an assessment of the worst severity of pain experienced during the 7 days after an eligible dental procedure—and the data was collected using an item from the validated Patient-Reported Outcomes Measurement Information System (PROMIS) Shortform 3A Version 1 questionnaire [ | PMC10644946 | |
Secondary Outcomes | PMC10644946 | |||
Pain Interference | Pain | Pain interference, defined as interference with activity (walking, work, general activity, sleep) and interference with affect (mood, enjoyment of life), was captured using 3 items taken from the validated Revised American Pain Society Patient Outcome Questionnaire (APS-POQ-R) form [ | PMC10644946 | |
Patient Satisfaction | pain | Satisfaction with how pain was managed was assessed with the following two statements from the validated APS-POQ-R form, which was measured on a 0 to 10 rating scale [Ability to participate in decisions about pain treatmentSatisfaction with the results of your pain treatment | PMC10644946 | |
Use of Opioid Medications | SECONDARY | The proportion of participating patients who got a postoperative opioid prescription was assessed using data from the patient EHR. Through secondary analysis of the EHR, medication-prescribing patterns were collected by deploying query scripts to identify the patients who received the prescriptions postoperatively, inc... | PMC10644946 | |
Sample Size | pain | Among the 2 included dental sites, a total of 42 providers were recruited to participate in the study over the 2-year study period. Each provider was expected to reasonably recruit 19 patients per year. The expected number of patients was 1596. Adjusting for a 60% response rate among recruited patients, we calculated a... | PMC10644946 | |
Statistical Methods | pain | Means and corresponding estimates of precision (eg, SDs and 95% CIs) and frequency distributions with percentage contributions were used to report the distribution of each variable included in the quantitative analyses. To test whether there was a difference in pain intensity, interference, or satisfaction with pain ma... | PMC10644946 | |
Fidelity | Fidelity was measured using metrics as outlined in | PMC10644946 | ||
Fidelity metrics for patients and providers. |
Provided verbal consent and received the information sheet.FollowApp.Care profile was createdReceived text notifications on day 0Patient response timeNumber of patients who have phone service provided by T-MobileResponse rate day 1Response rate day 3Response rate day 5Response rate day 7
Signed consent forms before tr... | PMC10644946 | ||
Assessing Provider Acceptance | ’ postoperative pain, postoperative acute pain, pain | To assess whether practitioners were unduly burdened by the technology and whether it fit seamlessly into their workflow, the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire was administered to those in the intervention group. Four key constructs were measured: performance expectancy, effort ex... | PMC10644946 | |
Ethical Considerations | The study protocol was reviewed and approved by the University of Texas Institutional Review Board (IRB# 18-25477) and registered on ClinicalTrials.gov (NCT03881891). Using a standardized template provided by the research team, providers or clinic staff members obtained informed consent from interested patients before ... | PMC10644946 | ||
Results | PMC10644946 | |||
Patient and Provider Population | A total of 42 providers (intervention: n=24; control: n=18), consisting of 24 general dentists, 16 endodontists, and 2 oral surgeons, participated in the trial. The study included 1525 patients (intervention: n=851; control: n=674) with an average age of 44.5 (SD 14.3) years, of whom 675 (44.3%) were female and 865 (56... | PMC10644946 | ||
Fidelity | Response rates for the mHealth-administered surveys were 56.9% (484/851) on day 1 and 49.8% (424/851) on day 7 for intervention patients, and 42% (283/674) for control patients. All patients had a FollowApp.Care profile created, and 98.3% (1504/1525) received the day 1 SMS text message notification, with an average res... | PMC10644946 | ||
Patient Satisfaction | pain | In response to the question “Were you allowed to participate in decisions about your pain treatment as much as you wanted to? (0, least to 10, most),” respondents in the intervention group reported an average of 7.7 (SD 3.5) out of 10 in participation in decision-making, while those in the control group reported an ave... | PMC10644946 | |
Adjusted Analysis | REGRESSION | The regression analysis showed that there was no statistically significant difference between the intervention and control arms for all study outcomes, after adjusting for provider, gender, and procedure type. | PMC10644946 | |
Provider Experience With the mHealth App | Results from the UTAUT questionnaire indicated that most providers found the platform useful, clear, and understandable; that their organization in general thought they should use it; and that they have the necessary resources and knowledge to use the platform. The validated UTAUT questionnaire was administered to 18 i... | PMC10644946 | ||
Qualitative Analyses | postoperative acute pain, anxiety, ’ | Three main themes were identified from the perspective of providers regarding the use of the platform for postoperative acute pain management: (1) potential facilitators and barriers to adoption, (2) patient acceptance and hesitancy, and (3) future use of the platform (It seemed like we were a lot more accessible. It w... | PMC10644946 | |
Discussion | PMC10644946 | |||
Principal Findings | Postoperative pain, postoperative acute pain, pain | CHRONIC PAIN, POSTOPERATIVE COMPLICATION | In this prospective, randomized, parallel-arm clinical trial evaluating the impact of an mHealth app on overall dental postoperative acute pain experience, we found no significant differences in pain experience or use of analgesic medication after painful dental procedures between the intervention (mHealth) and control... | PMC10644946 |
Limitations | pain | EMA | This study was conducted at two sites where standard postoperative care is exemplary, with disciplined adherence to evidence-based guidelines. Future studies should focus on pragmatic trials including sites that are more similar to everyday dental clinics with less stringent protocols, processes, or guidelines in place... | PMC10644946 |
Conclusion | pain | POSTOPERATIVE COMPLICATION | The study showed that using the mHealth platform did not have a significant impact on acute postoperative pain experience. However, patients and providers indicated increased improvements in patient-provider communication, patient-provider relationship, postoperative complication management, and the ability to manage p... | PMC10644946 |
Abbreviations | Pain | Revised American Pain Society Patient Outcome Questionnaireelectronic health recordecological momentary assessmentmobile healthpatient-reported outcomePatient-Reported Outcomes Measurement Information SystemUnified Theory of Acceptance and Use of Technology | PMC10644946 | |
References | Descriptive analysis for the Unified Theory of Acceptance and Use of Technology questionnaire.Thematic analysis: barriers and facilitators.CONSORT-eHEALTH checklist (V 1.6.1). | PMC10644946 | ||
Purpose | The use of electronic patient-reported outcome (ePRO) data in routine care has been tied to direct patient benefits such as improved quality of care and symptom control and even overall survival. The modes of action behind such benefits are seldom described in detail. Here, we describe the development of a model of car... | PMC10363070 | ||
Methods | Development was split into four stages: (1) identification of an underlying theoretical framework, (2) the selection of an ePRO measure (ePROM), (3) the adaptation of an electronic application to collect ePRO data, and (4) the description of an ePRO-oriented workflow. The model of care is currently evaluated in a bicen... | PMC10363070 | ||
Results | The IePRO model of care is grounded in the eHealth Enhanced Chronic Care Model. Patients are prompted to report symptoms using an electronic mobile application. Triage nurses are alerted, review the reported symptoms, and contact patients in case of a new or worsening symptom. Nurses use the UKONS 24-hour telephone tri... | PMC10363070 | ||
Conclusion | This report clarifies how components of care are created and modified to leverage ePRO to enhance care. The model describes a workflow that enables care teams to be proactive and provide patients with timely, multidisciplinary support to manage symptoms. | PMC10363070 | ||
Supplementary Information | The online version contains supplementary material available at 10.1007/s00520-023-07934-w. | PMC10363070 | ||
Keywords | Open access funding provided by University of Lausanne | PMC10363070 | ||
Introduction
| PROM, cancer | CANCER, DISEASE PROGRESSION | Immune checkpoint inhibitors (ICI) have become part of the standard of treatment for an expanding range of cancer types [These IrAE are notably heterogeneous, occasionally resembling disease progression and mimicking auto-immune conditions [Patient education and symptom self-management, particularly self-monitoring, co... | PMC10363070 |
Toward the development of an ePRO-based model of care | PROM | Development of the IePRO model of care took place between November 2020 and November 2021. A team of four physicians and five nurses of the participating institutions’ oncology departments and one patient-representative collaborated in the creation of its core components and their integration in the existing workflows ... | PMC10363070 | |
Theoretical framework | CCM, LMS, DSD | GEE | As ICI-related symptoms may add to the symptom burden of patients, effective management of these symptoms requires a holistic approach. To reflect upon and address the complexity and resources required for symptom management, we grounded the development of this intervention in the eHealth Enhanced Chronic Care Model (e... | PMC10363070 |
Selection of an ePROM | PROM, toxicity | ADVERSE EVENT | Active discussions between the model development team allowed to identify an ePROM of particular interest, to both clinicians and patients. The patient-representative mobilized her patient-advocacy network to collect and convey general perceptions on existing PROM, such as their perceived advantages and disadvantages t... | PMC10363070 |
Adaptation of an electronic mobile application | deterioration or disease progression | COMPLICATIONS | The main goal in using an ePRO application is to enhance self-management support (SMS). As an eCCM component, SMS includes the provision of tools and resources for patients to acquire the skills and confidence to manage and monitor their health condition [The application sends patient reminders to fill out the ePROM at... | PMC10363070 |
Development of an ePRO-oriented workflow and clinical roles | tumor, DSD | TUMOR, EVENT | In the eCCM, delivery system design (DSD) relates to how care is coordinated and delivered across the network of health resources. The participating oncology departments treat a similar range of tumor types and number of patients, with similar provider team compositions. Physicians and nurses involved in direct patient... | PMC10363070 |
Patient engagement | As in the eCCM, informed and activated patients are key to create productive interactions with the healthcare providers [Patients fill out the 37-item ePROM within the first week of ICI treatment by logging in to the online or mobile (smartphone) version of the application. They are prompted to complete subsequent dail... | PMC10363070 | ||
Telephone triage nurses and triage process | confusion | ONCOLOGY, EVENT | Telephone triage nurses are the main vector of communication between the patient and the clinical oncology team in the IePRO model. This role was developed and reviewed with oncology physicians, nurses, and CNS. For some oncology subspecialties, the CNS provide sporadic telephone consultations for the most vulnerable p... | PMC10363070 |
Role of physicians and other healthcare professionals | Physicians are the primary collaborators with the triage nurses and are responsible for reviewing triage reports. When their assessment differs from the nurse’s, the physician is to contact them and the patient to provide their recommendation. Triage nurses and physicians may also forward requests to other professional... | PMC10363070 | ||
Assessing usability of the ePRO application and acceptability of the model of care | Assessment of the usability of the ePRO application and the acceptability of the model of care from the patient’s perspective takes place up to two weeks after study discontinuation. The mobile application’s usability and the model of care’s acceptability are assessed through semi-structured interviews with patients. B... | PMC10363070 | ||
Discussion | PROM, tumor | MINOR, TUMOR | The IePRO model of care supports the detection and timely management of symptoms of patients treated with ICI. It represents a pragmatic research approach to the use of ePRO data in the context of two university hospitals that retain minor differences in resources and infrastructure, standard operating procedures, and ... | PMC10363070 |
Conclusion | The described based model of care provides insight into the complexity of using ePRO data to facilitate potential benefits for both patients and care providers. It attempts to draw a closed feedback loop between patients and providers, to ensure symptoms related to ICI treatments and beyond are monitored and managed by... | PMC10363070 | ||
Author contributions | All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by André Manuel da Silva Lopes, Sara Colomer-Lahiguera, and Manuela Eicher. The first draft of the manuscript was written by André Manuel da Silva Lopes, Sara Colomer-Lahiguera and Manuela Eich... | PMC10363070 |
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