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BackgroundData on effects of intra-gastric balloon (IGB) on metabolic dysfunction-associated steatotic liver disease (MASLD) are scarce, in part with contradictory results, and mainly obtained in tertiary care patients with diabetes and other comorbidities. We here explore effects of IGB in patients with MASLD referred to a first-line obesity clinic.MethodsIn this prospective cohort study, patients with at least significant fibrosis (≥ F2) and/or severe steatosis (S3) according to screening transient elastography (FibroScan®) were offered a second FibroScan® after 6 months lifestyle modification with or without IGB (based on patient preference). Results: 50 of 100 consecutively screened patients (generally non-diabetic) qualified for repeated evaluation and 29 (58%) of those had a second FibroScan®. At baseline, at least significant fibrosis was present in 28% and severe steatosis in 91%. IGB was placed in 19 patients (59%), whereas 10 patients (41%) preferred only lifestyle modification (no differences in baseline characteristics between both groups). After 6 months, liver stiffness decreased markedly in the IGB group (median: from 6.0 to 4.9 kPa, p = 0.005), but not in the lifestyle modification only group (median: from 5.5 to 6.9 kPa, p = 0.477). Steatosis improved in both groups, (controlled attenuation parameter values; IGB, mean ± SD: from 328 ± 34 to 272 ± 62 dB/m, p = 0.006: lifestyle modification only, mean ± SD: from 344 ± 33 to 305 ± 43 dB/m: p = 0.006).ConclusionBoth steatosis and fibrosis improve markedly in overweight/obese patients with MASLD after 6 months IGB combined with lifestyle modification. Our results warrant further research into long-term effect of IGB in these patients.

BackgroundPatients who display disruptive behaviours in the clinical encounter (the so-called ‘difficult patients’) may negatively affect doctors’ diagnostic reasoning, thereby causing diagnostic errors. The present study aimed at investigating the mechanisms underlying the negative influence of difficult patients’ behaviours on doctors’ diagnostic performance.MethodsA randomised experiment with 74 internal medicine residents. Doctors diagnosed eight written clinical vignettes that were exactly the same except for the patients’ behaviours (either difficult or neutral). Each participant diagnosed half of the vignettes in a difficult patient version and the other half in a neutral version in a counterbalanced design. After diagnosing each vignette, participants were asked to recall the patient's clinical findings and behaviours. Main measurements were: diagnostic accuracy scores; time spent on diagnosis, and amount of information recalled from patients’ clinical findings and behaviours.ResultsMean diagnostic accuracy scores (range 0–1) were significantly lower for difficult than neutral patients’ vignettes (0.41 vs 0.51; p<0.01). Time spent on diagnosing was similar. Participants recalled fewer clinical findings (mean=29.82% vs mean=32.52%; p<0.001) and more behaviours (mean=25.51% vs mean=17.89%; p<0.001) from difficult than from neutral patients.ConclusionsDifficult patients’ behaviours induce doctors to make diagnostic errors, apparently because doctors spend part of their mental resources on dealing with the difficult patients’ behaviours, impeding adequate processing of clinical findings. Efforts should be made to increase doctors’ awareness of the potential negative influence of difficult patients’ behaviours on diagnostic decisions and their ability to counteract such influence.

BackgroundLiterature suggests that patients who display disruptive behaviours in the consulting room fuel negative emotions in doctors. These emotions, in turn, are said to cause diagnostic errors. Evidence substantiating this claim is however lacking. The purpose of the present experiment was to study the effect of such difficult patients’ behaviours on doctors’ diagnostic performance.MethodsWe created six vignettes in which patients were depicted as difficult (displaying distressing behaviours) or neutral. Three clinical cases were deemed to be diagnostically simple and three deemed diagnostically complex. Sixty-three family practice residents were asked to evaluate the vignettes and make the patient's diagnosis quickly and then through deliberate reflection. In addition, amount of time needed to arrive at a diagnosis was measured. Finally, the participants rated the patient's likability.ResultsMean diagnostic accuracy scores (range 0–1) were significantly lower for difficult than for neutral patients (0.54 vs 0.64; p=0.017). Overall diagnostic accuracy was higher for simple than for complex cases. Deliberate reflection upon the case improved initial diagnostic, regardless of case complexity and of patient behaviours (0.60 vs 0.68, p=0.002). Amount of time needed to diagnose the case was similar regardless of the patient's behaviour. Finally, average likability ratings were lower for difficult than for neutral-patient cases.ConclusionsDisruptive behaviours displayed by patients seem to induce doctors to make diagnostic errors. Interestingly, the confrontation with difficult patients does however not cause the doctor to spend less time on such case. Time can therefore not be considered an intermediary between the way the patient is perceived, his or her likability and diagnostic performance.

Emergency department (ED) crowding leads to prolonged emergency department length of stay (ED-LOS) and adverse patient outcomes. No uniform definition of ED crowding exists. Several scores have been developed to quantify ED crowding; the best known is the Emergency Department Work Index (EDWIN). Research on the EDWIN is often applied to limited settings and conducted over a short period of time. To explore whether the EDWIN as a measure can track occupancy at a Dutch ED over the course of one year and to identify fluctuations in ED occupancy per hour, day, and month. Secondary objective is to investigate the discriminatory value of the EDWIN in detecting crowding, as compared with the occupancy rate and prolonged ED-LOS. A retrospective cohort study of all ED visits during the period from September 2010 to August 2011 was performed in one hospital in the Netherlands. The EDWIN incorporates the number of patients per triage level, physicians, treatment beds and admitted patients to quantify ED crowding. The EDWIN was adjusted to emergency care in the Netherlands: modified EDWIN (mEDWIN). ED crowding was defined as the 75th percentile of mEDWIN per hour, which was ≥0.28. In total, 28,220 ED visits were included in the analysis. The median mEDWIN per hour was 0.15 (Interquartile range (IQR) 0.05-0.28); median mEDWIN per patient was 0.25 (IQR 0.15-0.39). The EDWIN was higher on Wednesday (0.16) than on other days (0.14-0.16, p<0.001), and a peak in both mEDWIN (0.30-0.33) and ED crowding (52.9-63.4%) was found between 13:00-18:00 h. A comparison of the mEDWIN with the occupancy rate revealed an area under the curve (AUC) of 0.86 (95%CI 0.85-0.87). The AUC of mEDWIN compared with a prolonged ED-LOS (≥4 hours) was 0.50 (95%CI 0.40-0.60). The mEDWIN was applicable at a Dutch ED. The mEDWIN was able to identify fluctuations in ED occupancy. In addition, the mEDWIN had high discriminatory power for identification of a busy ED, when compared with the occupancy rate.

ObjectivesTo compare the quality and time efficiency of physician-written summaries with customised large language model (LLM)-generated medical summaries integrated into the electronic health record (EHR) in a non-English clinical environment.DesignCross-sectional non-inferiority validation study.SettingTertiary academic hospital.Participants52 physicians from 8 specialties at a large Dutch academic hospital participated, either in writing summaries (n=42) or evaluating them (n=10).InterventionsPhysician writers wrote summaries of 50 patient records. LLM-generated summaries were created for the same records using an EHR-integrated LLM. An independent, blinded panel of physician evaluators compared physician-written summaries to LLM-generated summaries.Primary and secondary outcome measuresPrimary outcome measures were completeness, correctness and conciseness (on a 5-point Likert scale). Secondary outcomes were preference and trust, and time to generate either the physician-written or LLM-generated summary.ResultsThe completeness and correctness of LLM-generated summaries did not differ significantly from physician-written summaries. However, LLM summaries were less concise (3.0 vs 3.5, p=0.001). Overall evaluation scores were similar (3.4 vs 3.3, p=0.373), with 57% of evaluators preferring LLM-generated summaries. Trust in both summary types was comparable, and interobserver variability showed excellent reliability (intraclass correlation coefficient 0.975). Physicians took an average of 7 min per summary, while LLMs completed the same task in just 15.7 s.ConclusionsLLM-generated summaries are comparable to physician-written summaries in completeness and correctness, although slightly less concise. With a clear time-saving benefit, LLMs could help reduce clinicians’ administrative burden without compromising summary quality.

This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial. In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel. Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4–8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7–92.8%). Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information.

AbstractObjectiveTo review and appraise the validity and usefulness of published and preprint reports of prediction models for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital or dying with the disease.DesignLiving systematic review and critical appraisal by the covid-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.Data sourcesPubMed and Embase through Ovid, up to 17 February 2021, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.Study selectionStudies that developed or validated a multivariable covid-19 related prediction model.Data extractionAt least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).Results126 978 titles were screened, and 412 studies describing 731 new prediction models or validations were included. Of these 731, 125 were diagnostic models (including 75 based on medical imaging) and the remaining 606 were prognostic models for either identifying those at risk of covid-19 in the general population (13 models) or predicting diverse outcomes in those individuals with confirmed covid-19 (593 models). Owing to the widespread availability of diagnostic testing capacity after the summer of 2020, this living review has now focused on the prognostic models. Of these, 29 had low risk of bias, 32 had unclear risk of bias, and 545 had high risk of bias. The most common causes for high risk of bias were inadequate sample sizes (n=408, 67%) and inappropriate or incomplete evaluation of model performance (n=338, 56%). 381 models were newly developed, and 225 were external validations of existing models. The reported C indexes varied between 0.77 and 0.93 in development studies with low risk of bias, and between 0.56 and 0.78 in external validations with low risk of bias. The Qcovid models, the PRIEST score, Carr’s model, the ISARIC4C Deterioration model, and the Xie model showed adequate predictive performance in studies at low risk of bias. Details on all reviewed models are publicly available at https://www.covprecise.org/.ConclusionPrediction models for covid-19 entered the academic literature to support medical decision making at unprecedented speed and in large numbers. Most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic. Models with low risk of bias should be validated before clinical implementation, preferably through collaborative efforts to also allow an investigation of the heterogeneity in their performance across various populations and settings. Methodological guidance, as provided in this paper, should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction modellers should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.Systematic review registrationProtocol https://osf.io/ehc47/, registration https://osf.io/wy245.Readers’ noteThis article is the final version of a living systematic review that has been updated over the past two years to reflect emerging evidence. This version is update 4 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Emergency department (ED) patients are triaged to determine the urgency of care. The Finapres Portapres (FP) measures blood pressure (BP) and cardiac output (CO) non-invasively, and may be of added value in early detection of patients at risk for hemodynamic compromise. Compare non-invasive BP measurements using FP and standard automated sphygmomanometry. Compare FP cardiac index (CI), CO corrected for body surface area, of normotensive patients, to chart-based physician estimate of shock, to discover if there is additional value in CI measurements in triage. ED Patients requiring BP measurement in triage were included. Systolic (SBP) and diastolic (DBP) BP were measured using both devices during a two minutes measurement. Two physicians independently judged probability of shock, defined as estimated CI ≤2.5 Lmin−1m−2, based on chart review, three weeks after ED visit. Of a total of 112 patients 97 patients were included. Pearson's correlation coefficient was 0.50 for SBP, 0.53 for DBP, with a Blant-Altman mean bias of 11.3 (upper limit 65.3, lower limit −42.8) and 7.7 (39.2, −23.7) for SBP and DBP respectively. In normotensive patients, the group with low FP CI measurements had significantly more cases with physician-estimated shock, compared to the normal to high measurements (P = .036). When used as a triage device in the emergency department setting, non-invasive BP measurements using FP do not correlate well with automated sphygmomanometry. However, this study does indicate that use of the FP device in triage may aid physicians to recognize patients in early phases of shock.

Emergency department (ED) crowding leads to prolonged emergency department length of stay (ED-LOS) and adverse patient outcomes. No uniform definition of ED crowding exists. Several scores have been developed to quantify ED crowding; the best known is the Emergency Department Work Index (EDWIN). Research on the EDWIN is often applied to limited settings and conducted over a short period of time. To explore whether the EDWIN as a measure can track occupancy at a Dutch ED over the course of one year and to identify fluctuations in ED occupancy per hour, day, and month. Secondary objective is to investigate the discriminatory value of the EDWIN in detecting crowding, as compared with the occupancy rate and prolonged ED-LOS. A retrospective cohort study of all ED visits during the period from September 2010 to August 2011 was performed in one hospital in the Netherlands. The EDWIN incorporates the number of patients per triage level, physicians, treatment beds and admitted patients to quantify ED crowding. The EDWIN was adjusted to emergency care in the Netherlands: modified EDWIN (mEDWIN). ED crowding was defined as the 75.sup.th percentile of mEDWIN per hour, which was [greater than or equal to]0.28. In total, 28,220 ED visits were included in the analysis. The median mEDWIN per hour was 0.15 (Interquartile range (IQR) 0.05-0.28); median mEDWIN per patient was 0.25 (IQR 0.15-0.39). The EDWIN was higher on Wednesday (0.16) than on other days (0.14-0.16, p<0.001), and a peak in both mEDWIN (0.30-0.33) and ED crowding (52.9-63.4%) was found between 13:00-18:00 h. A comparison of the mEDWIN with the occupancy rate revealed an area under the curve (AUC) of 0.86 (95%CI 0.85-0.87). The AUC of mEDWIN compared with a prolonged ED-LOS ([greater than or equal to]4 hours) was 0.50 (95%CI 0.40-0.60). The mEDWIN was applicable at a Dutch ED. The mEDWIN was able to identify fluctuations in ED occupancy. In addition, the mEDWIN had high discriminatory power for identification of a busy ED, when compared with the occupancy rate.