30-day hospital admission among older adults managed at home by a mobile emergency unit (MEU): a retrospective cohort study

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The aim is to avoid unnecessary hospital admissions and to reduce overcrowding in emergency departments. However, it is unknown which demographic, clinical, and paraclinical characteristics of patients at the index MEU assessment are related to subsequent hospital admission. Therefore, we aimed to describe these baseline characteristics and to examine their association with 30-day hospital admission. Methods In this retrospective, single-centre cohort study at Esbjerg Hospital (Region of Southern Denmark), we screened 1,656 MEU contacts (from 1 January to 31 December 2024) and included adults aged ≥ 65 years, who were managed at home. These patients were potential candidates for hospital admission, and the emergency physician made an on-scene decision regarding admission. Data were analysed using multivariable logistic regression. Results We included 357 MEU contacts, with a median (interquartile range) age of 83.5 (11.7) years. 140 (39.2%) of these contacts were admitted to hospital within 30 days. A higher proportion of the admitted patients had a pre-existing do-not-attempt-resuscitation (DNAR) order compared with the non-admitted patients (85.0% vs 66.4%; p < 0.001) and lived at home (57.8% vs. 47.4%; p = 0.055). Chronic pulmonary disease was more common among the admitted patients (31.4% vs 19.3%; p = 0.009), whereas dementia was less frequent (18.6% vs 28.1%; p = 0.042). Both a pre-existing DNAR order (odds ratio [OR] 3.83, 95% confidence interval [CI] 2.05–7.16) and home (vs nursing home) residence (OR 1.76, 95% CI 1.03–2.98) were significantly associated with hospital admission in the adjusted model. Conclusions Among older adults assessed at home by MEU physicians, a pre-existing DNAR order and home (vs nursing home) residence were independently associated with 30-day hospital admission. These findings may inform triage and follow-up planning. However, prospective studies are required to establish causal links. Prehospital care Home-based assessment Emergency medicine Older adults Hospital admission Mobile emergency unit Figures Figure 1 Background The global population is ageing rapidly: by 2050, the number of adults aged 60 years and above is projected to double [ 1 ]. This situation has led to major challenges for healthcare systems, particularly emergency departments (EDs) [ 2 – 4 ]. Older adults are especially vulnerable to acute illness, multimorbidity, and polypharmacy, which increase their risk of adverse hospital-related outcomes, including delirium, nosocomial infections, functional decline, and mortality [ 5 – 9 ]. Denmark has aimed to address this situation by implementing mobile emergency unit (MEU). As part of this prehospital initiative, an emergency medicine (EM) physician and nurse conduct clinical assessments and initiate treatment in the patient’s residence. This approach aims to prevent unnecessary hospital admissions and reduce the associated risks with admission [ 10 – 14 ]. Emerging data suggest that evaluation by MEU allows a substantial proportion of older adults to be managed safely at home following the initial assessment and intervention [ 14 ]. There has been extensive work to predict hospital admission, readmission, or length of stay. Researchers have developed prognostic models based on ED triage data, prehospital records, and/or post-discharge information. Many of these models depend on demographic characteristics, comorbidity indices, and vital signs [ 15 – 20 ]. However, there are some important issues with these models. First, there has been limited work on developing models specifically tailored to older adults, a particularly relevant issue given population ageing. A notable example is an ED-based tool proposed by Abugroun et al. [ 21 ] that is tailored to older adults. It integrates both clinical and functional measures to support decisions as to whether patients should be admitted to hospital. Second, it would also be useful to integrate findings from point-of-care ultrasound (POCUS) and point-of-care testing (POCT) into these models. Currently, POCUS is generally used to assess specific conditions (e.g., dyspnoea), and its ability to stratify older adults based on hospital admission risk has not been widely evaluated [ 22 – 26 ]. Likewise, POCT offers rapid diagnostics but has seldom been combined with other prehospital data to create a comprehensive risk model. It is crucial to identify higher-risk patients at the time of a home visit because it could enable more timely hospital admission when needed. On the other hand, lower-risk patients could be managed safely at home with appropriate follow-up. Although this approach would improve both patient safety and resource allocation – especially for older adults – it requires extensive knowledge about the specific baseline characteristics of higher-risk patients. No prior study has examined the association between baseline features recorded during EM physician–led home visits and subsequent hospital admission among patients initially managed at home. Therefore, we aimed to compare the baseline demographic, clinical, and paraclinical characteristics between older adults with and without 30-day hospital admission after an MEU home-based assessment, and to estimate adjusted associations using multivariable logistic regression. Methods Study design and setting We conducted a retrospective, single-centre cohort study and reported the results in accordance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidance [ 27 ]. We screened all MEU contacts between 1 January 2024 and 31 December 2024 in the catchment area of Esbjerg Hospital, Region of Southern Denmark. The MEU covers approximately 3,400 km² and serves a population of about 240,000. Description of the MEU service MEU-based care, operated by the Esbjerg Hospital ED, forms an integral part of the regional prehospital care system. It is staffed by an EM physician and a nurse and is available daily from 08:00 to 18:00. There are two ways to activate the MEU: referral from a general practitioner, on-call physician, or nursing home to the ED admission coordinator; or direct activation by the emergency medical dispatch centre if a 1-1-2 call meets the MEU eligibility criteria. Upon arrival, the MEU conducts an on-site emergency assessment. They may initiate treatment (e.g., intravenous antibiotics or fluids) and retain medical responsibility for up to 96 hours after their visit, with follow-up visits as clinically indicated. The team works in collaboration with municipal acute care nurses, who continue prescribed medication or monitoring, and escalate to the ED if the patient deteriorates. Participants We screened all 1,656 MEU contacts during the study period and patients were excluded based on the following criteria (Fig. 1 ): • Age < 65 years; • When patients were not considered candidates for hospital admission due to pre-existing comorbidities, a severely reduced functional status, or limited life expectancy, or were receiving palliative care for terminal illness; • Incomplete or missing MEU documentation in the electronic health record (EHR); • Death during the index visit or within 30 days; • Contact limited to a telephone consultation (no physical assessment); • Only technical or supportive procedures performed without clinical assessment or decision-making (e.g., peripheral venous catheter, urinary catheter, nasogastric tube, blood sampling, wound care, or wound assessment); • Assessment for low-energy trauma (e.g., head or extremity injury) where the team concluded it was caused by a mechanical fall and did not perform further diagnostics; • Referral solely for deep vein thrombosis (DVT) scanning without other acute complaints or interventions; When a patient had multiple MEU visits, we only included the first visit. If a subsequent visit occurred more than 30 days later, we considered it a new case. [Figure 1 near here] Data sources and variables We extracted data from EHRs and, when needed, cross-checked their completeness and accuracy against the emergency medical dispatch centre records (e.g., visit times and patient identifiers). We collected the following variables: • Demographics, including age, sex, place of residence (home or nursing home), date of death (if applicable), smoking status, and alcohol use disorder; • The do-not-attempt-resuscitation (DNAR) status based on documented decisions prior to the MEU visit; • Comorbidities, including hypertension, diabetes mellitus, cancer, dementia, and chronic pulmonary disease. We summarised multimorbidity according to the number of comorbid conditions (0, 1–2, or ≥ 3); • Data from the index visit, including the reason for referral and use/findings of POCT and POCUS (obtained from clinical notes when available). Note that when the chief complaints are cardiological, the patients are generally routed directly to hospital. Thus, cardiological patients are under-represented in our sample. The case mix primarily reflects respiratory, urinary, neurological, and other presentations. Outcome The primary outcome was 30-day hospital admission following initial home management. We defined this as the first hospital admission within 30 days among patients who were initially assessed by the MEU and judged suitable to remain at home. We determined the outcome from the EHRs and administrative records. Data categorisation Because many of the available measurements were either charted qualitatively or not recorded, we coded all physiological parameters (systolic blood pressure, oxygen saturation, heart rate, temperature, and the Glasgow Coma Scale [GCS] score) and tests (POCT and POCUS) as three-level categorical variables: normal, abnormal, or not performed. We defined the abnormal values by using prespecified cut-offs: systolic blood pressure of < 100 mmHg, oxygen saturation of < 92% (or < 88% in patients with documented chronic pulmonary disease), heart rate of 100 beats per minute (bpm), temperature of ≥ 38°C, and a GCS score of < 15. We excluded the respiratory rate because it was missing for a large number of cases. We retained the not performed category because the omission of tests may reflect clinical judgement and thus carry prognostic information. Statistical analysis We used IBM SPSS Statistics, version 26 (IBM Corp., Armonk, NY, USA) for all data analysis. The categorical variables are presented as counts and percentage, while the continuous variables are summarised as the mean or median and interquartile range (IQR), as appropriate. We used the Mann–Whitney U test and the χ² test to compare between groups. We also performed univariable logistic regression for descriptive purposes. Candidate predictors were prespecified a priori based on clinical relevance and the literature and were included in a multivariable binary logistic regression model (i.e., we did not select them solely based on the p-values from univariable regression). Ethical considerations This study was approved as a quality-improvement project by the Esbjerg Hospital administration and registered in the internal registry of the Region of Southern Denmark. In line with Danish legislation and guidance from the National Committee on Health Research Ethics, this retrospective, non-interventional study did not require formal approval by an ethics committee. The study complied with the Declaration of Helsinki and the General Data Protection Regulation. Results In total, we included 357 contacts that met the eligibility criteria. Table 1 shows the baseline characteristics of the patients. The median (IQR) age of all patients was 83.5 (11.7) years. Age did not differ significantly between those admitted to hospital within 30 days of the index visit (81.8 [IQR 13.1] years) and those not admitted (84.2 [IQR 11] years; p = 0.334). Of the included contacts, 162 (45.4%) were men and 195 (54.6%) were women. The gender distribution did not differ significantly between those who were admitted to hospital within 30 days of the index visit and those who were not ( p = 0.908). Table 1. Baseline characteristics of the study population stratified by 30-day hospital admission status Not admitted (n = 217) Admitted (n = 140) p -value Age, years 84.2 [11.0] 81.8 [13.1] 0.334 Sex Male Female 99 (45.6) 118 (54.4) 63 (45) 77 (55) 0.908 Place of residence Home Nursing home 103 (47.4) 114 (53) 81 (57.8) 59 (42.2) 0.055 Pre-existing DNAR No Yes 73 (33.6) 144 (66.4) 21 (15) 119 (85) <0.001 Hypertension Yes No 110 (50.7) 107 (49.3) 77 (55) 63 (45) 0.426 Diabetes mellitus Yes No 42 (19.3) 175 (80.7) 39 (27.9) 101 (72.1) 0.061 Cancer No Prior Metastatic 176 (81.1) 34 (15.7) 7 (3.2) 108 (77.1) 24 (17.1) 8 (5.8) 0.464 Chronic pulmonary disease Yes No 42 (19.3) 175 (80.7) 44 (31.4) 96 (68.6) 0.009 Dementia Yes No 61 (28.1) 156 (71.9) 26 (18.6) 114 (81.4) 0.042 Comorbidity burden 0 1–2 ≥3 45 (20.7) 155 (71.4) 17 (7.9) 23 (16.4) 97 (69.3) 20 (14.3) 0.116 Smoking status Current smoker Former smoker Never smoker Unknown 18 (8.3) 62 (28.6) 48 (22.1) 89 (41) 24 (17.1) 50 (35.7) 30 (21.5) 36 (25.7) 0.005 Alcohol use disorder Yes No or unknown 9 (4.1) 208 (95.9) 11 (7.9) 129 (92.1) 0.137 Primary presentation complaint Respiratory Urinary Neurological Other 85 (39.2) 23 (10.6) 36 (16.6) 73 (33.6) 56 (40) 16 (11.4) 21 (15) 47 (33.6) 0.977 Data are presented as median [IQR] or n (%).DNAR, do-not-attempt-resuscitation; IQR, interquartile range; Univariable analysis Univariable logistic regression (Table 2) revealed that the following variables led to higher odds of 30-day hospital admission: chronic pulmonary disease (odds ratio [OR] 1.91, 95% confidence interval [CI] 1.16–3.11; p = 0.010), a pre-existing DNAR order (OR 2.87, 95% CI 1.67–4.94; p < 0.001), and a comorbidity burden ≥3 versus 0 (OR 2.30, 95% CI 1.01–5.22; p = 0.046). Dementia was associated with lower odds of 30-day hospital admission (OR 0.58, 95% CI 0.34–0.98; p = 0.042) and an abnormal POCT finding with higher odds (OR 1.63, 95% CI 1.01–2.64; p = 0.045). Home (vs nursing-home) residence showed a trend for higher odds of 30-day hospital admission (OR 1.51, 95% CI 0.99–2.32; p = 0.055). The other variables were not significantly associated with 30-day admission ( p > 0.05). [Table 2 near here] Multivariable logistic regression We fitted a multivariable logistic regression model using the following prespecified baseline predictors: age, sex, place of residence, pre-existing DNAR, the smoking status (unknown, former, or current vs never), alcohol use disorder, comorbidity burden, diabetes mellitus, chronic pulmonary disease, dementia, and the POCT category (abnormal or not performed vs normal). The model was statistically significant (χ² [df = 15] = 50.65, p < 0.001), showed acceptable fit (Hosmer–Lemeshow χ² [df = 8] = 8.89, p = 0.352), and demonstrated moderate explanatory power (Nagelkerke R² = 0.181). Based on the model, two factors showed an independent association with 30-day admission: home (vs nursing-home) residence (OR 1.76, 95% CI 1.03–2.98, p = 0.037) and a pre-existing DNAR order (OR 3.83, 95% CI 2.05–7.16, p 0.05) (Table 3). Table 2. Univariable logistic regression analysis for predictors of 30- day hospital admission OR 95% CI p -value Age (per year) 0.98 0.96–1.01 0.308 Sex Male (baseline) Female 1.02 0.66–1.57 0.908 Place of residence Nursing home (baseline) Home 1.51 0.99–2.32 0.055 Hypertension No (baseline) Yes 1.18 0.77–1.82 0.426 Diabetes mellitus No (baseline) Yes 1.61 0.97–2.65 0.062 Cancer No (baseline) Prior Metastatic 1.15 1.86 0.64–2.04 0.65–5.28 0.633 0.242 Chronic pulmonary disease No (baseline) Yes 1.91 1.16–3.11 0.010 Dementia No (baseline) Yes 0.58 0.34–0.98 0.042 Comorbidity burden 0 (baseline) 1–2 ≥3 1.22 2.31 0.69–2.15 1.01–5.22 0.481 0.046 Pre-existing DNAR No (baseline) Yes 2.87 1.67–4.94 <0.001 Smoking status Never (baseline) Unknown Former Current 0.64 1.29 2.13 0.35–1.17 0.71–2.32 0.99–4.57 0.154 0.396 0.051 Alcohol use disorder No (baseline) Active or former 1.97 0.79–4.88 0.143 Temperature Normal (baseline) Abnormal Not documented/not performed 1.08 0.85 0.56–2.11 0.51–1.43 0.803 0.555 Oxygen saturation Normal (baseline) Abnormal Not documented/not performed 1.18 1.05 0.63–2.24 0.61–1.81 0.594 0.838 Blood pressure Normal (baseline) Abnormal Not documented/not performed 2.05 1.36 0.85–4.92 0.77–2.38 0.106 0.283 GCS score Normal (baseline) Abnormal Not documented/not performed 1.21 0.96 0.74–­1.99 0.49–1.88 0.436 0.911 Heart rate Normal (baseline) Abnormal Not documented/not performed 1.06 1.48 0.49–2.28 0.86–2.56 0.879 0.153 POCT Normal (baseline) Abnormal Not documented/not performed 1.63 1.79 1.01–2.64 0.91–3.49 0.045 0.088 POCUS Normal (baseline) Abnormal Not documented/not performed 2.04 1.45 0.95–4.36 0.86–2.44 0.065 0.162 Odds ratios (OR) and 95% confidence intervals (CI) from univariable logistic regression models. CI, confidence interval; DNAR, do-not-attempt-resuscitation; OR, odds ratio; POCT, point-of-care testing; POCUS, point-of-care ultrasound. Table 3. Multivariable logistic regression identifying independent predictors of 30- day hospital admission OR 95% CI p -value Age 0.98 0.95–1.01 0.370 Sex Male (baseline) Female 1.19 0.73–1.95 0.470 Place of residence Nursing home (baseline) Home 1.76 1.03–2.98 0.037 Diabetes mellitus No (baseline) Yes 1.51 0.73–3.06 0.262 Chronic pulmonary disease No (baseline) Yes 1.09 0.55–2.14 0.793 Dementia No (baseline) Yes 0.73 0.37–1.43 0.372 Comorbidity burden 0 (baseline) 1–2 ≥3 0.98 1.47 0.48–1.98 0.41–5.34 0.956 0.553 Pre-existing DNAR No (baseline) Yes 3.83 2.05–7.16 <0.001 Smoking status Never (baseline) Unknown Former Current 0.58 0.91 1.69 0.29–1.14 0.45–1.81 0.66–4.31 0.117 0.789 0.267 Alcohol use disorder No (baseline) Yes 0.57 0.20–1.64 0.305 POCT Normal (baseline) Abnormal Not documented/not performed 1.66 1.92 0.98–2.82 0.93–3.96 0.059 0.076 OR, odds ratio; CI, confidence interval; DNAR, do-not-attempt-resuscitation; POCT, point-of-care testing. Discussion In this retrospective cohort study, we identified variables associated with 30-day hospital admission among older adults assessed and initially managed at home by the MEU, and notably, we found that a pre-existing DNAR order, home (vs nursing home) residence, and an abnormal POCT finding were associated with increased odds of admission. These findings highlight the clinical and contextual factors that shape post-evaluation trajectories in this novel prehospital care setting. There have been equivocal findings regarding the association of the DNAR status with hospitalisation and mortality. Differently from our study, Mehta et al. [ 28 ] reported that an early DNAR order in patients hospitalised with pneumonia was associated with a reduced risk of unplanned 30-day readmission. However, in the surgical setting, a newly established DNAR order during hospitalisation was linked to substantially higher postoperative mortality and increased rates of serious complications, including pneumonia, stroke, and myocardial infarction [ 29 ]. Similarly, Sheehan et al. [ 30 ] demonstrated that patients who had a DNAR order upon hospital admission had significantly higher in-hospital mortality and lower utilisation of intensive interventions compared with those without a DNAR order. The discrepancy between our findings and the literature may be explained by differences in patient populations, study designs, and care pathways. In our study, the DNAR order was already in place before the MEU contacted the patient, so the EM physician had no role in defining it. Moreover, we assessed the effect of this factor in relation to initial hospital admission following prehospital home evaluation. In contrast, most previous work has focused on the risk of readmission or the outcomes after hospitalisation or surgery. A prehospital DNAR order may serve as a proxy for frailty and limited physiological reserve in our patient cohort, indicating that this group is likely more vulnerable. In the prehospital triage context, such vulnerability could plausibly increase the risk of hospital admission when an acute or a critical illness arises. To our knowledge, few if any studies have directly examined the relationship between a pre-existing DNAR order and initial admission decisions in prehospital cohorts. Given the lack of information available in the literature, additional studies should be performed to confirm our findings and to explore whether the DNAR status should be incorporated into prehospital risk-stratification tools to enhance clinical decision-making and resource allocation. It is also important to note that in the Danish context, a DNAR order does not imply that a patient cannot be admitted to hospital or can only receive limited care. A DNAR order only specifies the approach to cardiopulmonary resuscitation in the event of cardiac arrest. Many patients with a pre-existing DNAR order still receive a full diagnostic work-up and active treatment, including hospital admission, when it is clinically indicated. The decision as to whether a patient is a candidate for admission due to advanced comorbidity or frailty are documented separately (e.g., treatment-limitation or ceiling-of-care notes) and are distinct from the DNAR status. Our model also suggests that compared with community-dwelling older adults, nursing home residents have lower odds of hospital admission 30 days after an MEU contact. Consistently, Kristensen et al. [ 31 ] and Krüger et al. [ 32 ] found that nursing home residents have a lower or equivalent risk of readmission or hospitalisation compared with the general older adult population. This reduced risk can be explained by the availability of trained healthcare staff at nursing homes who are always available to perform routine monitoring as well as basic acute interventions. These factors ensure that nursing home residents receive timely care and reduce the need for hospital transfers. By contrast, community-dwelling older adults lack continuous monitoring, which may increase the risk of admission following an MEU contact. These observations align with the systematic review by Konetzka et al. [ 33 ], who found that higher healthcare staff-to-patient ratio is the strongest factor associated with reduced hospitalisation of nursing home residents. Based on these differences, it may be useful to check on community-dwelling older adults in the period immediately following an MEU contact. This structured ‘enhanced home care’ could involve daily nursing check-ins, remote monitoring of vital signs, and rapid escalation pathways during the first 72–96 hours to mitigate early deterioration and to reduce unplanned admissions. Prospective trials are necessary to evaluate whether this approach could improve safety and decrease 30-day hospitalisation in community-dwelling older adults. Our study has several strengths. We focused on a novel care model in Denmark, analysed a relatively large patient cohort, and were able to comprehensively gather prehospital clinical variables. Because we excluded terminally ill patients and those directly admitted to hospital, our cohort provides a more accurate reflection of the target population in which prehospital admission risk stratification is clinically relevant. Finally, our work is novel: to our knowledge, few prior studies have specifically examined hospital admission following prehospital emergency physician assessment at home. We must also acknowledge several limitations of our study. First, the retrospective, single-centre design may limit the generalisability of our findings. Thus, our findings need to be validated in other regions before the identified associations can be generalised or incorporated into clinical tools. Second, we analysed multiple predictors, but we cannot exclude the effect of unmeasured confounding variables. Specifically, our exclusion of patients who died within 30 days may have introduced competing-risk bias because death can occur before and preclude admission. Consequently, our estimates should be interpreted as associations rather than causal effects, again highlighting the need for prospective studies. Conclusion Among older adults assessed at home by emergency physicians, a pre-existing DNAR order and the residence status emerged as independent factors associated with 30-day hospital admission. Incorporating these factors into decision-making algorithms could enhance prehospital triage by improving the accuracy of identifying patients at risk of subsequent hospital admission. Our findings can support, but not replace, clinical judgement and require confirmation in prospective studies. Future studies should seek to validate these predictors in larger, multicentre cohorts and explore whether integrating such variables into clinical tools can optimise resource allocation, reduce unnecessary admissions, and support safe home-based management. Moreover, the use of larger datasets may also enable incorporation of additional physiological and clinical parameters (e.g. vital signs and POCT findings). Declarations Ethics approval and consent to participate This study was approved as a quality-improvement project by the Esbjerg Hospital administration and registered in the internal registry of the Region of Southern Denmark. In line with Danish legislation and guidance from the National Committee on Health Research Ethics, this retrospective, non-interventional study did not require formal approval by an ethics committee. Consent for publication Not applicable Consent to Participate declaration As this was a retrospective study using existing records only, individual informed consent was not required Availability of data and materials Due to GDPR and local institutional policies, the dataset cannot be publicly shared. De-identified data are available from the corresponding author on reasonable request and subject to approval by the Region of Southern Denmark. Competing interests The authors declare no competing interests. Funding No external funding. The study was conducted as part of routine quality improvement within the Emergency Department at Esbjerg Hospital. Acknowledgements We thank the MEU clinicians and the ED administrative team at Esbjerg Hospital for support with data access. 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Feretzakis G, Karlis G, Loupelis E, Kalles D, Chatzikyriakou R, Trakas N, et al. Using machine learning techniques to predict hospital admission at the emergency department. J Crit Care Med (Targu Mureș). 2022;8(2):107–16. 10.2478/jccm-2022-0003 . Abugroun A, Awadalla S, Singh S, Fang MC. Development of an emergency department triage tool to predict admission or discharge for older adults. Int J Emerg Med. 2025;18:26. 10.1186/s12245-025-00825-3 . Gundersen EA, Juhl-Olsen P, Bach A, Rostgaard-Knudsen M, Nielsen BR, Skaarup SH, et al. Prehospital ultrasound in undifferentiated dyspnoea (PreLUDE): a prospective observational study. Scand J Trauma Resusc Emerg Med. 2023;31:6. 10.1186/s13049-023-01070-4 . Pietersen PI, Mikkelsen S, Lassen AT, Helmerik S, Jørgensen G, Nadim G, et al. Quality of focused thoracic ultrasound performed by emergency medical technicians and paramedics in a prehospital setting: a feasibility study. Scand J Trauma Resusc Emerg Med. 2021;29(1):40. 10.1186/s13049-021-00856-8 . Sen JPB, Emerson J, Franklin J. Diagnostic accuracy of prehospital ultrasound in detecting lung injury in patients with trauma: a systematic review and meta-analysis. Emerg Med J. 2025;42(4):256–63. 10.1136/emermed-2023-213647 . Lin K-T, Lin Z-Y, Huang C-C, Yu S-Y, Huang J-L, Lin J-H, et al. Prehospital ultrasound scanning for abdominal free fluid detection in trauma patients: a systematic review and meta-analysis. BMC Emerg Med. 2024;24:7. 10.1186/s12873-023-00919-2 . Vianen NJ, van Lieshout EMM, Vlasveld KHA, Maissan IM, Gerritsen PC, den Hartog D, et al. Impact of point-of-care ultrasound on prehospital decision-making by HEMS physicians: a prospective cohort study. Prehosp Disaster Med. 2023;38(4):444–9. 10.1017/S1049023X23006003 . von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, STROBE Initiative. The STROBE statement. PLoS Med. 2007;4(10):e296. 10.1371/journal.pmed.0040296 . Mehta AB, Cooke CR, Douglas IS, Lindenauer PK, Wiener RS, Walkey AJ. Association of early do-not-resuscitate orders with unplanned readmissions among patients hospitalized for pneumonia. Ann Am Thorac Soc. 2017;14(1):103–9. 10.1513/AnnalsATS.201608-617OC . Brovman EY, Motejunas MW, Bonneval LA, Whang EE, Kaye AD, Urman RD. Relationship between newly established perioperative DNR status and perioperative outcomes in the elderly population: a NSQIP database analysis. J Palliat Care. 2024;39(2):97–104. 10.1177/0825859720944746 . Sheehan MM, Zilberberg MD, Lindenauer PK, Higgins TL, Imrey PB, Guo N, et al. Associations between present-on-admission do-not-resuscitate orders and short-term outcomes in patients with pneumonia. South Med J. 2024;117(3):165–71. 10.14423/SMJ.0000000000001663 . Kristensen GS, Søndergaard J, Andersen-Ranberg K, Mogensen CB. Acute readmissions among care home residents aged 65 + years: a register-based study. Eur Geriatr Med. 2025;16(3):827–38. 10.1007/s41999-025-01162-7 . Krüger K, Jansen K, Grimsmo A, Eide GE, Geitung JT. Hospital admissions from nursing homes: rates and reasons. Nurs Res Pract. 2011;2011:247623. 10.1155/2011/247623 . Konetzka RT, Spector W, Limcangco MR. Reducing hospitalizations from long-term care settings. Med Care Res Rev. 2008;65(1):40–66. 10.1177/1077558707307569 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2026 Read the published version in BMC Emergency Medicine → Version 1 posted Editorial decision: Revision requested 20 Jan, 2026 Reviews received at journal 18 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 26 Oct, 2025 Editor assigned by journal 17 Oct, 2025 Submission checks completed at journal 17 Oct, 2025 First submitted to journal 14 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Moradi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYNCCCiBmBjF4GBj42ME0swF+LWeQtLAxE6OFsQ2JQ1CL7rTDxz78nHdPTt6d+djDHzJ2cmzMzM8evGGwNsalxex2WvLM3m3FxoaH2dKNeXiSjdmY2cwN5zCkm+HWkmPMwLstIXFjM4+ZNAMPc2IbM4OZNA/DYRt8Whj/zkmoB2mR/MFTX9/GzP6NoBZm3oaEBHlmHjMJHp7DCWxABkgLHoelJTPLHEsw3MDMlibNw3PcsI2Zp0xyjkE6Hu8nH2Z8U5MgL99/+Jjkz55qeX729m0SbyqsDRtw6YEBgwNAgrEHziWkHgjkwYb+IELlKBgFo2AUjDgAAIbVRuqdGPv/AAAAAElFTkSuQmCC","orcid":"","institution":"University Hospital of Southern Denmark Esbjerg","correspondingAuthor":true,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Moradi","suffix":""},{"id":532649319,"identity":"1e21b2a3-6c49-4985-b48d-741f4eb1e3d6","order_by":1,"name":"Anita Maués Østergaard Pedersen","email":"","orcid":"","institution":"University Hospital of Southern Denmark Esbjerg","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"Maués Østergaard","lastName":"Pedersen","suffix":""},{"id":532649320,"identity":"411ff946-b2f9-48c8-a3e8-41a24f1f4bbd","order_by":2,"name":"Katrine Jaquet Mavraganis","email":"","orcid":"","institution":"University Hospital of Southern Denmark Esbjerg","correspondingAuthor":false,"prefix":"","firstName":"Katrine","middleName":"Jaquet","lastName":"Mavraganis","suffix":""},{"id":532649323,"identity":"416d98a7-f22f-4922-9a3b-6554262601b6","order_by":3,"name":"Mette Rahbek Kristensen","email":"","orcid":"","institution":"University Hospital of Southern Denmark Esbjerg","correspondingAuthor":false,"prefix":"","firstName":"Mette","middleName":"Rahbek","lastName":"Kristensen","suffix":""},{"id":532649325,"identity":"1daf56d1-0502-4022-a437-8df3b8c17905","order_by":4,"name":"Johanne Overgaard Wessels","email":"","orcid":"","institution":"University Hospital of Southern Denmark Esbjerg","correspondingAuthor":false,"prefix":"","firstName":"Johanne","middleName":"Overgaard","lastName":"Wessels","suffix":""},{"id":532649327,"identity":"b3c8ee6d-a654-473a-9e67-178439b3e8e1","order_by":5,"name":"Sina Bayatshahbazi","email":"","orcid":"","institution":"Basildon University Hospital, Mid and South Essex NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Sina","middleName":"","lastName":"Bayatshahbazi","suffix":""},{"id":532649328,"identity":"d3b46133-90ab-49f4-9cd1-f0df1366ff7f","order_by":6,"name":"Peter Biesenbach","email":"","orcid":"","institution":"University Hospital of Southern Denmark Esbjerg","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Biesenbach","suffix":""}],"badges":[],"createdAt":"2025-10-14 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14:10:49","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119539,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7861485/v1/9e24d887ef72975874f8dfd0.html"},{"id":94202785,"identity":"6828ba14-4437-487e-aab2-01e31fbb870f","added_by":"auto","created_at":"2025-10-23 14:10:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":234451,"visible":true,"origin":"","legend":"\u003cp\u003eStudy cohort selection and exclusions from MEU contacts\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend. \u003c/strong\u003eFlow diagram showing screening of 1,656 Mobile Emergency Unit (MEU) contacts in 2024 and reasons for exclusion, resulting in 357 index contacts analyzed for the primary outcome. DVT, Deep Vein Thrombosis; MEU, Mobile Emergency Unit.\u003c/p\u003e","description":"","filename":"Figure.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7861485/v1/8549b2398339e4148c9bdcf2.jpg"},{"id":104739549,"identity":"8dda5da4-dd51-4e10-bfbd-b4d6ec87a0ec","added_by":"auto","created_at":"2026-03-16 16:09:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1250153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7861485/v1/36d0d5ac-21e3-4c19-9f8b-dbf8e02f7d0b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"30-day hospital admission among older adults managed at home by a mobile emergency unit (MEU): a retrospective cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe global population is ageing rapidly: by 2050, the number of adults aged 60 years and above is projected to double [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This situation has led to major challenges for healthcare systems, particularly emergency departments (EDs) [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Older adults are especially vulnerable to acute illness, multimorbidity, and polypharmacy, which increase their risk of adverse hospital-related outcomes, including delirium, nosocomial infections, functional decline, and mortality [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Denmark has aimed to address this situation by implementing mobile emergency unit (MEU). As part of this prehospital initiative, an emergency medicine (EM) physician and nurse conduct clinical assessments and initiate treatment in the patient\u0026rsquo;s residence. This approach aims to prevent unnecessary hospital admissions and reduce the associated risks with admission [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Emerging data suggest that evaluation by MEU allows a substantial proportion of older adults to be managed safely at home following the initial assessment and intervention [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere has been extensive work to predict hospital admission, readmission, or length of stay. Researchers have developed prognostic models based on ED triage data, prehospital records, and/or post-discharge information. Many of these models depend on demographic characteristics, comorbidity indices, and vital signs [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, there are some important issues with these models. First, there has been limited work on developing models specifically tailored to older adults, a particularly relevant issue given population ageing. A notable example is an ED-based tool proposed by Abugroun et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] that is tailored to older adults. It integrates both clinical and functional measures to support decisions as to whether patients should be admitted to hospital. Second, it would also be useful to integrate findings from point-of-care ultrasound (POCUS) and point-of-care testing (POCT) into these models. Currently, POCUS is generally used to assess specific conditions (e.g., dyspnoea), and its ability to stratify older adults based on hospital admission risk has not been widely evaluated [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Likewise, POCT offers rapid diagnostics but has seldom been combined with other prehospital data to create a comprehensive risk model.\u003c/p\u003e\u003cp\u003eIt is crucial to identify higher-risk patients at the time of a home visit because it could enable more timely hospital admission when needed. On the other hand, lower-risk patients could be managed safely at home with appropriate follow-up. Although this approach would improve both patient safety and resource allocation \u0026ndash; especially for older adults \u0026ndash; it requires extensive knowledge about the specific baseline characteristics of higher-risk patients. No prior study has examined the association between baseline features recorded during EM physician\u0026ndash;led home visits and subsequent hospital admission among patients initially managed at home. Therefore, we aimed to compare the baseline demographic, clinical, and paraclinical characteristics between older adults with and without 30-day hospital admission after an MEU home-based assessment, and to estimate adjusted associations using multivariable logistic regression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective, single-centre cohort study and reported the results in accordance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We screened all MEU contacts between 1 January 2024 and 31 December 2024 in the catchment area of Esbjerg Hospital, Region of Southern Denmark. The MEU covers approximately 3,400 km\u0026sup2; and serves a population of about 240,000.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDescription of the MEU service\u003c/h3\u003e\n\u003cp\u003eMEU-based care, operated by the Esbjerg Hospital ED, forms an integral part of the regional prehospital care system. It is staffed by an EM physician and a nurse and is available daily from 08:00 to 18:00. There are two ways to activate the MEU: referral from a general practitioner, on-call physician, or nursing home to the ED admission coordinator; or direct activation by the emergency medical dispatch centre if a 1-1-2 call meets the MEU eligibility criteria. Upon arrival, the MEU conducts an on-site emergency assessment. They may initiate treatment (e.g., intravenous antibiotics or fluids) and retain medical responsibility for up to 96 hours after their visit, with follow-up visits as clinically indicated. The team works in collaboration with municipal acute care nurses, who continue prescribed medication or monitoring, and escalate to the ED if the patient deteriorates.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe screened all 1,656 MEU contacts during the study period and patients were excluded based on the following criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Age\u0026thinsp;\u0026lt;\u0026thinsp;65 years;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; When patients were not considered candidates for hospital admission due to pre-existing comorbidities, a severely reduced functional status, or limited life expectancy, or were receiving palliative care for terminal illness;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Incomplete or missing MEU documentation in the electronic health record (EHR);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Death during the index visit or within 30 days;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Contact limited to a telephone consultation (no physical assessment);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Only technical or supportive procedures performed without clinical assessment or decision-making (e.g., peripheral venous catheter, urinary catheter, nasogastric tube, blood sampling, wound care, or wound assessment);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Assessment for low-energy trauma (e.g., head or extremity injury) where the team concluded it was caused by a mechanical fall and did not perform further diagnostics;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Referral solely for deep vein thrombosis (DVT) scanning without other acute complaints or interventions;\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eWhen a patient had multiple MEU visits, we only included the first visit. If a subsequent visit occurred more than 30 days later, we considered it a new case.\u003c/p\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e near here]\u003c/p\u003e\n\u003ch3\u003eData sources and variables\u003c/h3\u003e\n\u003cp\u003eWe extracted data from EHRs and, when needed, cross-checked their completeness and accuracy against the emergency medical dispatch centre records (e.g., visit times and patient identifiers). We collected the following variables:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Demographics, including age, sex, place of residence (home or nursing home), date of death (if applicable), smoking status, and alcohol use disorder;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; The do-not-attempt-resuscitation (DNAR) status based on documented decisions prior to the MEU visit;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Comorbidities, including hypertension, diabetes mellitus, cancer, dementia, and chronic pulmonary disease. We summarised multimorbidity according to the number of comorbid conditions (0, 1\u0026ndash;2, or \u0026ge;\u0026thinsp;3);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Data from the index visit, including the reason for referral and use/findings of POCT and POCUS (obtained from clinical notes when available).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eNote that when the chief complaints are cardiological, the patients are generally routed directly to hospital. Thus, cardiological patients are under-represented in our sample. The case mix primarily reflects respiratory, urinary, neurological, and other presentations.\u003c/p\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was 30-day hospital admission following initial home management. We defined this as the first hospital admission within 30 days among patients who were initially assessed by the MEU and judged suitable to remain at home. We determined the outcome from the EHRs and administrative records.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData categorisation\u003c/h2\u003e\u003cp\u003eBecause many of the available measurements were either charted qualitatively or not recorded, we coded all physiological parameters (systolic blood pressure, oxygen saturation, heart rate, temperature, and the Glasgow Coma Scale [GCS] score) and tests (POCT and POCUS) as three-level categorical variables: normal, abnormal, or not performed. We defined the abnormal values by using prespecified cut-offs: systolic blood pressure of \u0026lt;\u0026thinsp;100 mmHg, oxygen saturation of \u0026lt;\u0026thinsp;92% (or \u0026lt;\u0026thinsp;88% in patients with documented chronic pulmonary disease), heart rate of \u0026lt;\u0026thinsp;50 or \u0026gt;\u0026thinsp;100 beats per minute (bpm), temperature of \u0026ge;\u0026thinsp;38\u0026deg;C, and a GCS score of \u0026lt;\u0026thinsp;15. We excluded the respiratory rate because it was missing for a large number of cases. We retained the not performed category because the omission of tests may reflect clinical judgement and thus carry prognostic information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe used IBM SPSS Statistics, version 26 (IBM Corp., Armonk, NY, USA) for all data analysis. The categorical variables are presented as counts and percentage, while the continuous variables are summarised as the mean or median and interquartile range (IQR), as appropriate. We used the Mann\u0026ndash;Whitney U test and the χ\u0026sup2; test to compare between groups. We also performed univariable logistic regression for descriptive purposes. Candidate predictors were prespecified a priori based on clinical relevance and the literature and were included in a multivariable binary logistic regression model (i.e., we did not select them solely based on the p-values from univariable regression).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e This study was approved as a quality-improvement project by the Esbjerg Hospital administration and registered in the internal registry of the Region of Southern Denmark. In line with Danish legislation and guidance from the National Committee on Health Research Ethics, this retrospective, non-interventional study did not require formal approval by an ethics committee. The study complied with the Declaration of Helsinki and the General Data Protection Regulation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, we included 357 contacts that met the eligibility criteria. Table 1 shows the baseline characteristics of the patients. The median (IQR) age of all patients was 83.5 (11.7) years. Age did not differ significantly between those admitted to hospital within 30 days of the index visit (81.8 [IQR 13.1] years) and those not admitted (84.2 [IQR 11] years; \u003cem\u003ep\u003c/em\u003e = 0.334). Of the included contacts, 162 (45.4%) were men and 195 (54.6%) were women. The gender distribution did not differ significantly between those who were admitted to hospital within 30 days of the index visit and those who were not (\u003cem\u003ep\u003c/em\u003e = 0.908).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of the study population stratified by 30-day hospital admission status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003eNot admitted (n\u0026nbsp;=\u0026nbsp;217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003eAdmitted (n = 140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eAge, years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e84.2\u0026nbsp;[11.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e81.8\u0026nbsp;[13.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99\u0026nbsp;(45.6)\u003c/p\u003e\n \u003cp\u003e118\u0026nbsp;(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63\u0026nbsp;(45)\u003c/p\u003e\n \u003cp\u003e77\u0026nbsp;(55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003cp\u003eNursing\u0026nbsp;home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e103\u0026nbsp;(47.4)\u003c/p\u003e\n \u003cp\u003e114\u0026nbsp;(53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81\u0026nbsp;(57.8)\u003c/p\u003e\n \u003cp\u003e59\u0026nbsp;(42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003ePre-existing DNAR\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e73\u0026nbsp;(33.6)\u003c/p\u003e\n \u003cp\u003e144\u0026nbsp;(66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21\u0026nbsp;(15)\u003c/p\u003e\n \u003cp\u003e119\u0026nbsp;(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e110\u0026nbsp;(50.7)\u003c/p\u003e\n \u003cp\u003e107\u0026nbsp;(49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77\u0026nbsp;(55)\u003c/p\u003e\n \u003cp\u003e63\u0026nbsp;(45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42\u0026nbsp;(19.3)\u003c/p\u003e\n \u003cp\u003e175\u0026nbsp;(80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39\u0026nbsp;(27.9)\u003c/p\u003e\n \u003cp\u003e101\u0026nbsp;(72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003ePrior\u003c/p\u003e\n \u003cp\u003eMetastatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e176\u0026nbsp;(81.1)\u003c/p\u003e\n \u003cp\u003e34\u0026nbsp;(15.7)\u003c/p\u003e\n \u003cp\u003e7\u0026nbsp;(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e108\u0026nbsp;(77.1)\u003c/p\u003e\n \u003cp\u003e24\u0026nbsp;(17.1)\u003c/p\u003e\n \u003cp\u003e8\u0026nbsp;(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42\u0026nbsp;(19.3)\u003c/p\u003e\n \u003cp\u003e175\u0026nbsp;(80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44\u0026nbsp;(31.4)\u003c/p\u003e\n \u003cp\u003e96\u0026nbsp;(68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e61\u0026nbsp;(28.1)\u003c/p\u003e\n \u003cp\u003e156\u0026nbsp;(71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26\u0026nbsp;(18.6)\u003c/p\u003e\n \u003cp\u003e114\u0026nbsp;(81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eComorbidity burden\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u0026ndash;2\u003c/p\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45\u0026nbsp;(20.7)\u003c/p\u003e\n \u003cp\u003e155\u0026nbsp;(71.4)\u003c/p\u003e\n \u003cp\u003e17\u0026nbsp;(7.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23\u0026nbsp;(16.4)\u003c/p\u003e\n \u003cp\u003e97\u0026nbsp;(69.3)\u003c/p\u003e\n \u003cp\u003e20\u0026nbsp;(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003cp\u003eCurrent\u0026nbsp;smoker\u003c/p\u003e\n \u003cp\u003eFormer\u0026nbsp;smoker\u003c/p\u003e\n \u003cp\u003eNever\u0026nbsp;smoker\u003c/p\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18\u0026nbsp;(8.3)\u003c/p\u003e\n \u003cp\u003e62\u0026nbsp;(28.6)\u003c/p\u003e\n \u003cp\u003e48\u0026nbsp;(22.1)\u003c/p\u003e\n \u003cp\u003e89\u0026nbsp;(41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24\u0026nbsp;(17.1)\u003c/p\u003e\n \u003cp\u003e50\u0026nbsp;(35.7)\u003c/p\u003e\n \u003cp\u003e30\u0026nbsp;(21.5)\u003c/p\u003e\n \u003cp\u003e36\u0026nbsp;(25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003eAlcohol use disorder\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;or unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9\u0026nbsp;(4.1)\u003c/p\u003e\n \u003cp\u003e208\u0026nbsp;(95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11\u0026nbsp;(7.9)\u003c/p\u003e\n \u003cp\u003e129\u0026nbsp;(92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.604%;\"\u003e\n \u003cp\u003ePrimary presentation complaint\u003c/p\u003e\n \u003cp\u003eRespiratory\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUrinary\u003c/p\u003e\n \u003cp\u003eNeurological\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e85\u0026nbsp;(39.2)\u003c/p\u003e\n \u003cp\u003e23\u0026nbsp;(10.6)\u003c/p\u003e\n \u003cp\u003e36\u0026nbsp;(16.6)\u003c/p\u003e\n \u003cp\u003e73\u0026nbsp;(33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6273%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56\u0026nbsp;(40)\u003c/p\u003e\n \u003cp\u003e16\u0026nbsp;(11.4)\u003c/p\u003e\n \u003cp\u003e21\u0026nbsp;(15)\u003c/p\u003e\n \u003cp\u003e47\u0026nbsp;(33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4825%;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as median [IQR] or n (%).DNAR, do-not-attempt-resuscitation; IQR, interquartile range;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable logistic regression (Table 2) revealed that the following variables led to higher odds of 30-day hospital admission: chronic pulmonary disease (odds ratio [OR] 1.91, 95% confidence interval [CI] 1.16\u0026ndash;3.11; \u003cem\u003ep\u003c/em\u003e = 0.010), a pre-existing DNAR order (OR 2.87, 95% CI 1.67\u0026ndash;4.94; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and a comorbidity burden \u0026ge;3 versus 0 (OR 2.30, 95% CI 1.01\u0026ndash;5.22; \u003cem\u003ep\u003c/em\u003e = 0.046). Dementia was associated with lower odds of 30-day hospital admission (OR 0.58, 95% CI 0.34\u0026ndash;0.98; \u003cem\u003ep\u003c/em\u003e = 0.042) and an abnormal POCT finding with higher odds (OR 1.63, 95% CI 1.01\u0026ndash;2.64; \u003cem\u003ep\u003c/em\u003e = 0.045). Home (vs nursing-home) residence showed a trend for higher odds of 30-day hospital admission (OR 1.51, 95% CI 0.99\u0026ndash;2.32; \u003cem\u003ep\u003c/em\u003e = 0.055). The other variables were not significantly associated with 30-day admission (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). [Table 2 near here]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elogistic regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe fitted a multivariable logistic regression model using the following prespecified baseline predictors: age, sex, place of residence, pre-existing DNAR, the smoking status (unknown, former, or current vs never), alcohol use disorder, comorbidity burden, diabetes mellitus, chronic pulmonary disease, dementia, and the POCT category (abnormal or not performed vs normal). The model was statistically significant (\u0026chi;\u0026sup2; [df = 15] = 50.65, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), showed acceptable fit (Hosmer\u0026ndash;Lemeshow \u0026chi;\u0026sup2; [df = 8] = 8.89, \u003cem\u003ep\u003c/em\u003e = 0.352), and demonstrated moderate explanatory power (Nagelkerke R\u0026sup2; = 0.181). Based on the model, two factors showed an independent association with 30-day admission: home (vs nursing-home) residence (OR 1.76, 95% CI 1.03\u0026ndash;2.98, \u003cem\u003ep\u003c/em\u003e = 0.037) and a pre-existing DNAR order (OR 3.83, 95% CI 2.05\u0026ndash;7.16, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The other variables were not independently associated after adjustment (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Univariable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elogistic regression analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003epredictors\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of 30-\u003c/strong\u003e\u003cstrong\u003eday hospital admission\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eAge (per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e0.96\u0026ndash;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eMale\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.66\u0026ndash;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eNursing home (baseline)\u003c/p\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.99\u0026ndash;2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eNo (baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.77\u0026ndash;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003cp\u003eNo (baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.97\u0026ndash;2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003cp\u003eNo (baseline)\u003c/p\u003e\n \u003cp\u003ePrior\u003c/p\u003e\n \u003cp\u003eMetastatic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.64\u0026ndash;2.04\u003c/p\u003e\n \u003cp\u003e0.65\u0026ndash;5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003cp\u003eNo (baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.16\u0026ndash;3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003cp\u003eNo (baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.34\u0026ndash;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eComorbidity burden\u003c/p\u003e\n \u003cp\u003e0\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003e1\u0026ndash;2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.69\u0026ndash;2.15\u003c/p\u003e\n \u003cp\u003e1.01\u0026ndash;5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003ePre-existing DNAR\u003c/p\u003e\n \u003cp\u003eNo (baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.67\u0026ndash;4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003cp\u003eNever (baseline)\u003c/p\u003e\n \u003cp\u003eUnknown\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.35\u0026ndash;1.17\u003c/p\u003e\n \u003cp\u003e0.71\u0026ndash;2.32\u003c/p\u003e\n \u003cp\u003e0.99\u0026ndash;4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eAlcohol use disorder\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Active or former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u0026ndash;4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eTemperature\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNormal (baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot documented/not\u0026nbsp;performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.56\u0026ndash;2.11\u003c/p\u003e\n \u003cp\u003e0.51\u0026ndash;1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eOxygen saturation\u003c/p\u003e\n \u003cp\u003eNormal\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot documented/not\u0026nbsp;performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.63\u0026ndash;2.24\u003c/p\u003e\n \u003cp\u003e0.61\u0026ndash;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eBlood pressure\u003c/p\u003e\n \u003cp\u003eNormal (baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot documented/not\u0026nbsp;performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.85\u0026ndash;4.92\u003c/p\u003e\n \u003cp\u003e0.77\u0026ndash;2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eGCS\u0026nbsp;score\u003c/p\u003e\n \u003cp\u003eNormal (baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot documented/not\u0026nbsp;performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.74\u0026ndash;\u0026shy;1.99\u003c/p\u003e\n \u003cp\u003e0.49\u0026ndash;1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eHeart rate\u003c/p\u003e\n \u003cp\u003eNormal (baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot documented/not\u0026nbsp;performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.49\u0026ndash;2.28\u003c/p\u003e\n \u003cp\u003e0.86\u0026ndash;2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003ePOCT\u003c/p\u003e\n \u003cp\u003eNormal (baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot documented/not\u0026nbsp;performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.01\u0026ndash;2.64\u003c/p\u003e\n \u003cp\u003e0.91\u0026ndash;3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003ePOCUS\u003c/p\u003e\n \u003cp\u003eNormal\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot documented/not\u0026nbsp;performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.95\u0026ndash;4.36\u003c/p\u003e\n \u003cp\u003e0.86\u0026ndash;2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOdds ratios (OR) and 95% confidence intervals (CI) from univariable logistic regression models. CI, confidence interval; DNAR, do-not-attempt-resuscitation; OR, odds ratio; POCT, point-of-care testing; POCUS, point-of-care ultrasound.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multivariable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003elogistic regression identifying independent predictors\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of 30-\u003c/strong\u003e\u003cstrong\u003eday hospital admission\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e0.95\u0026ndash;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eMale (baseline)\u003c/p\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.73\u0026ndash;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eNursing\u0026nbsp;home (baseline)\u003c/p\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.03\u0026ndash;2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.73\u0026ndash;3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.55\u0026ndash;2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.37\u0026ndash;1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eComorbidity burden\u003c/p\u003e\n \u003cp\u003e0 (baseline)\u003c/p\u003e\n \u003cp\u003e1\u0026ndash;2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026ge;3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.48\u0026ndash;1.98\u003c/p\u003e\n \u003cp\u003e0.41\u0026ndash;5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003ePre-existing DNAR\u003c/p\u003e\n \u003cp\u003eNo (baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.05\u0026ndash;7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003cp\u003eNever\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eUnknown\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.29\u0026ndash;1.14\u003c/p\u003e\n \u003cp\u003e0.45\u0026ndash;1.81\u003c/p\u003e\n \u003cp\u003e0.66\u0026ndash;4.31\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003eAlcohol use disorder\u003c/p\u003e\n \u003cp\u003eNo\u0026nbsp;(baseline)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.20\u0026ndash;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.4293%;\"\u003e\n \u003cp\u003ePOCT\u003c/p\u003e\n \u003cp\u003eNormal (baseline)\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eNot\u0026nbsp;documented/not performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3045%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98\u0026ndash;2.82\u003c/p\u003e\n \u003cp\u003e0.93\u0026ndash;3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds ratio; CI, confidence interval; DNAR, do-not-attempt-resuscitation; POCT, point-of-care testing.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study, we identified variables associated with 30-day hospital admission among older adults assessed and initially managed at home by the MEU, and notably, we found that a pre-existing DNAR order, home (vs nursing home) residence, and an abnormal POCT finding were associated with increased odds of admission. These findings highlight the clinical and contextual factors that shape post-evaluation trajectories in this novel prehospital care setting.\u003c/p\u003e\u003cp\u003eThere have been equivocal findings regarding the association of the DNAR status with hospitalisation and mortality. Differently from our study, Mehta et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported that an early DNAR order in patients hospitalised with pneumonia was associated with a reduced risk of unplanned 30-day readmission. However, in the surgical setting, a newly established DNAR order during hospitalisation was linked to substantially higher postoperative mortality and increased rates of serious complications, including pneumonia, stroke, and myocardial infarction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, Sheehan et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] demonstrated that patients who had a DNAR order upon hospital admission had significantly higher in-hospital mortality and lower utilisation of intensive interventions compared with those without a DNAR order.\u003c/p\u003e\u003cp\u003eThe discrepancy between our findings and the literature may be explained by differences in patient populations, study designs, and care pathways. In our study, the DNAR order was already in place before the MEU contacted the patient, so the EM physician had no role in defining it. Moreover, we assessed the effect of this factor in relation to initial hospital admission following prehospital home evaluation. In contrast, most previous work has focused on the risk of readmission or the outcomes after hospitalisation or surgery. A prehospital DNAR order may serve as a proxy for frailty and limited physiological reserve in our patient cohort, indicating that this group is likely more vulnerable. In the prehospital triage context, such vulnerability could plausibly increase the risk of hospital admission when an acute or a critical illness arises. To our knowledge, few if any studies have directly examined the relationship between a pre-existing DNAR order and initial admission decisions in prehospital cohorts. Given the lack of information available in the literature, additional studies should be performed to confirm our findings and to explore whether the DNAR status should be incorporated into prehospital risk-stratification tools to enhance clinical decision-making and resource allocation.\u003c/p\u003e\u003cp\u003eIt is also important to note that in the Danish context, a DNAR order does not imply that a patient cannot be admitted to hospital or can only receive limited care. A DNAR order only specifies the approach to cardiopulmonary resuscitation in the event of cardiac arrest. Many patients with a pre-existing DNAR order still receive a full diagnostic work-up and active treatment, including hospital admission, when it is clinically indicated. The decision as to whether a patient is a candidate for admission due to advanced comorbidity or frailty are documented separately (e.g., treatment-limitation or ceiling-of-care notes) and are distinct from the DNAR status.\u003c/p\u003e\u003cp\u003eOur model also suggests that compared with community-dwelling older adults, nursing home residents have lower odds of hospital admission 30 days after an MEU contact. Consistently, Kristensen et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and Kr\u0026uuml;ger et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] found that nursing home residents have a lower or equivalent risk of readmission or hospitalisation compared with the general older adult population. This reduced risk can be explained by the availability of trained healthcare staff at nursing homes who are always available to perform routine monitoring as well as basic acute interventions. These factors ensure that nursing home residents receive timely care and reduce the need for hospital transfers. By contrast, community-dwelling older adults lack continuous monitoring, which may increase the risk of admission following an MEU contact. These observations align with the systematic review by Konetzka et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], who found that higher healthcare staff-to-patient ratio is the strongest factor associated with reduced hospitalisation of nursing home residents. Based on these differences, it may be useful to check on community-dwelling older adults in the period immediately following an MEU contact. This structured \u0026lsquo;enhanced home care\u0026rsquo; could involve daily nursing check-ins, remote monitoring of vital signs, and rapid escalation pathways during the first 72\u0026ndash;96 hours to mitigate early deterioration and to reduce unplanned admissions. Prospective trials are necessary to evaluate whether this approach could improve safety and decrease 30-day hospitalisation in community-dwelling older adults.\u003c/p\u003e\u003cp\u003eOur study has several strengths. We focused on a novel care model in Denmark, analysed a relatively large patient cohort, and were able to comprehensively gather prehospital clinical variables. Because we excluded terminally ill patients and those directly admitted to hospital, our cohort provides a more accurate reflection of the target population in which prehospital admission risk stratification is clinically relevant. Finally, our work is novel: to our knowledge, few prior studies have specifically examined hospital admission following prehospital emergency physician assessment at home.\u003c/p\u003e\u003cp\u003eWe must also acknowledge several limitations of our study. First, the retrospective, single-centre design may limit the generalisability of our findings. Thus, our findings need to be validated in other regions before the identified associations can be generalised or incorporated into clinical tools. Second, we analysed multiple predictors, but we cannot exclude the effect of unmeasured confounding variables. Specifically, our exclusion of patients who died within 30 days may have introduced competing-risk bias because death can occur before and preclude admission. Consequently, our estimates should be interpreted as associations rather than causal effects, again highlighting the need for prospective studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong older adults assessed at home by emergency physicians, a pre-existing DNAR order and the residence status emerged as independent factors associated with 30-day hospital admission. Incorporating these factors into decision-making algorithms could enhance prehospital triage by improving the accuracy of identifying patients at risk of subsequent hospital admission. Our findings can support, but not replace, clinical judgement and require confirmation in prospective studies. Future studies should seek to validate these predictors in larger, multicentre cohorts and explore whether integrating such variables into clinical tools can optimise resource allocation, reduce unnecessary admissions, and support safe home-based management. Moreover, the use of larger datasets may also enable incorporation of additional physiological and clinical parameters (e.g. vital signs and POCT findings).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved as a quality-improvement project by the Esbjerg Hospital administration and registered in the internal registry of the Region of Southern Denmark. In line with Danish legislation and guidance from the National Committee on Health Research Ethics, this retrospective, non-interventional study did not require formal approval by an ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this was a retrospective study using existing records only, individual informed consent was not required\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to GDPR and local institutional policies, the dataset cannot be publicly shared. De-identified data are available from the corresponding author on reasonable request and subject to approval by the Region of Southern Denmark.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No external funding. The study was conducted as part of routine quality improvement within the Emergency Department at Esbjerg Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;We thank the MEU clinicians and the ED administrative team at Esbjerg Hospital for support with data access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eList of abbreviations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMEU, DNAR, POCT, POCUS, ED, EHR, DVT, GCS, OR, CI, EM, IQR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMM and PB designed the research. MM, AMP, KJM, JOW and MRK performed the data search with the hospital data management service. MM analyzed the data. MM wrote the draft manuscript. PB supervised the study. MM, PB, JOW and SB contributed editorial revisions to the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Ageing and health. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/ageing-and-health\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/ageing-and-health\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 Jun 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLadehoff Thomsen AM, Tayyari N, Duvald I, Kirkegaard H, Obel B, Nielsen CP. Hospital at home for elderly acute patients: a study protocol for a randomised controlled trial. 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Med Care Res Rev. 2008;65(1):40\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1077558707307569\u003c/span\u003e\u003cspan address=\"10.1177/1077558707307569\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prehospital care, Home-based assessment, Emergency medicine, Older adults, Hospital admission, Mobile emergency unit","lastPublishedDoi":"10.21203/rs.3.rs-7861485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7861485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn recent years, Denmark has introduced mobile emergency unit (MEU) to provide patients with home-based evaluation and treatment by emergency medicine physicians. The aim is to avoid unnecessary hospital admissions and to reduce overcrowding in emergency departments. However, it is unknown which demographic, clinical, and paraclinical characteristics of patients at the index MEU assessment are related to subsequent hospital admission. Therefore, we aimed to describe these baseline characteristics and to examine their association with 30-day hospital admission.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective, single-centre cohort study at Esbjerg Hospital (Region of Southern Denmark), we screened 1,656 MEU contacts (from 1 January to 31 December 2024) and included adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, who were managed at home. These patients were potential candidates for hospital admission, and the emergency physician made an on-scene decision regarding admission. Data were analysed using multivariable logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe included 357 MEU contacts, with a median (interquartile range) age of 83.5 (11.7) years. 140 (39.2%) of these contacts were admitted to hospital within 30 days. A higher proportion of the admitted patients had a pre-existing do-not-attempt-resuscitation (DNAR) order compared with the non-admitted patients (85.0% vs 66.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lived at home (57.8% vs. 47.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055). Chronic pulmonary disease was more common among the admitted patients (31.4% vs 19.3%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), whereas dementia was less frequent (18.6% vs 28.1%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). Both a pre-existing DNAR order (odds ratio [OR] 3.83, 95% confidence interval [CI] 2.05\u0026ndash;7.16) and home (vs nursing home) residence (OR 1.76, 95% CI 1.03\u0026ndash;2.98) were significantly associated with hospital admission in the adjusted model.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAmong older adults assessed at home by MEU physicians, a pre-existing DNAR order and home (vs nursing home) residence were independently associated with 30-day hospital admission. These findings may inform triage and follow-up planning. However, prospective studies are required to establish causal links.\u003c/p\u003e","manuscriptTitle":"30-day hospital admission among older adults managed at home by a mobile emergency unit (MEU): a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 14:10:44","doi":"10.21203/rs.3.rs-7861485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-20T05:06:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-18T09:02:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86638910500398393555865659958500903738","date":"2026-01-09T08:49:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T21:18:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70341316352322980099917942754799016370","date":"2025-11-13T00:04:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143400727014840094940972464492068145402","date":"2025-11-12T14:43:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144002106434053333736760296226905828991","date":"2025-11-03T09:42:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-26T15:50:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-17T12:27:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-17T12:25:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2025-10-14T19:05:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df02c196-20dc-42ea-a74e-638bff2cd778","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:04:24+00:00","versionOfRecord":{"articleIdentity":"rs-7861485","link":"https://doi.org/10.1186/s12873-026-01536-5","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2026-03-11 16:00:19","publishedOnDateReadable":"March 11th, 2026"},"versionCreatedAt":"2025-10-23 14:10:44","video":"","vorDoi":"10.1186/s12873-026-01536-5","vorDoiUrl":"https://doi.org/10.1186/s12873-026-01536-5","workflowStages":[]},"version":"v1","identity":"rs-7861485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7861485","identity":"rs-7861485","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}