Diagnosing serious infections in older adults presenting to ambulatory care: a systematic review

Recognition of Serious Infections in the Elderly Visiting the Emergency Department: The Development of a Diagnostic Prediction Model (ROSIE)

Background/Objectives: Serious infections in older adults are associated with substantial mortality and morbidity. Diagnosis is challenging because of the non-specific presentation and overlap with pre-existing comorbidities. The objective of this study was to develop a clinical prediction model using clinical features and biomarkers to support emergency care physicians in diagnosing serious infections in acutely ill older adults. Methods: We conducted a prospective cross-sectional diagnostic study, consecutively including acutely ill patients (≥65 year) presenting to the emergency department. Clinical information and blood samples were collected at inclusion by a trained study nurse. A prediction model for any serious infection was developed based on ten candidate predictors that were further reduced to four ad interim using a penalized Firth multivariable logistic regression model. We assessed discrimination and calibration of the model after internal validation using bootstrapping. Results: We included 425 participants at three emergency departments, of whom 215 were diagnosed with a serious infection (51%). In the final model, we retained systolic blood pressure, oxygen saturation, and C-reactive protein as predictors. This model had good discriminatory value with an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.82 (95% CI: 0.78 to 0.86) and a calibration slope of 0.96 (95% CI: 0.76 to 1.16) after internal validation. Addition of procalcitonin did not improve the discrimination of the model. Conclusions: The ROSIE model uses three predictors that can be easily and quickly measured in the emergency department. It provides good discriminatory power after internal validation. Next steps should include external validation and an impact assessment.

Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department

OBJECTIVE

To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.

METHODS

This retrospective study included adults who visited the ED afebrile at initial presentation. We developed four models based on artificial neural networks to identify suspected serious infection. Patient demographics, vital signs, laboratory test results and information extracted from initial ED physician notes using term frequency-inverse document frequency were used as model variables. Models were trained and internally validated with data from one hospital and externally validated using data from a different hospital. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs).

RESULTS

The training, internal validation, and external validation datasets comprised 150,699, 37,675, and 85,098 patients, respectively. The AUCs (95% CIs) for Models 1 (demographics + vital signs), 2 (demographics + vital signs + initial ED physician note), 3 (demographics + vital signs + laboratory tests), and 4 (demographics + vital signs + laboratory tests + initial ED physician note) in the internal validation dataset were 0.789 (0.782-0.796), 0.867 (0.862-0.872), 0.881 (0.876-0.887), and 0.911 (0.906-0.915), respectively. In the external validation dataset, the AUCs (95% CIs) of Models 1, 2, 3, and 4 were 0.824 (0.817-0.830), 0.895 (0.890-0.899), 0.879 (0.873-0.884), and 0.913 (0.909-0.917), respectively. Model 1 can be utilized immediately after ED triage, Model 2 can be utilized after the initial physician notes are recorded (median time from ED triage: 28 min), and Models 3 and 4 can be utilized after the initial laboratory tests are reported (median time from ED triage: 68 min).

CONCLUSIONS

We developed and validated models to identify suspected serious infection in the ED. Extracted information from initial ED physician notes using NLP contributed to increased model performance, permitting identification of suspected serious infection at early stages of ED visits.

Improving diagnostics using extended point-of-care testing during in-home assessments of older adults with signs of emerging acute disease: a prospective observational non-randomised pilot and feasibility study

BACKGROUND

Delayed recognition of acute disease among older adults hinders timely management and increases the risk of hospital admission. Point-of-Care testing, including Focused Lung Ultrasound (FLUS) and in-home analysis of biological material, may support clinical decision-making in suspected acute respiratory disease. The aim of this study was to pilot test the study design for a planned randomised trial, investigate whether in-home extended use of point-of-care testing is feasible, and explore its' potential clinical impact.

METHODS

A non-randomised pilot and feasibility study was conducted during September-November 2021 in Kolding Municipality, Denmark. A FLUS-trained physician accompanied an acute community nurse on home-visits to citizens aged 65 + y with signs of acute respiratory disease. The acute community nurses did a clinical assessment (vital signs, capillary C-reactive protein and haemoglobin) and gave a presumptive diagnosis. Subsequently, the physician performed FLUS, venipuncture with bedside analysis (electrolytes, creatinine, white blood cell differential count), nasopharyngeal swab (PCR for upper respiratory pathogens), and urine samples (flow-cytometry). Primary outcomes were feasibility of study design and extended point-of-care testing; secondary outcome was the potential clinical impact of extended point-of-care testing.

RESULTS

One hundred consecutive individuals were included. Average age was 81.6 (SD ± 8.4). Feasibility of study design was acceptable, FLUS 100%, blood-analyses 81%, PCR for upper respiratory pathogens 79%, and urine flow-cytometry 4%. In addition to the acute community nurse's presumptive diagnosis, extended point-of-care testing identified 34 individuals with a condition in need of further evaluation by a physician.

CONCLUSION

Overall, in-home assessments with extended point-of-care testing are feasible and may aid to identify and handle acute diseases in older adults.

Extended use of point-of-care technology versus usual care for in-home assessment by acute community nurses in older adults with signs of potential acute respiratory disease: an open-label randomised controlled trial protocol

BACKGROUND

Due to ageing-related physiological changes, diagnosing older adults is challenging. Delayed disease recognition may lead to adverse health outcomes and increased hospitalisation, necessitating the development of new initiatives for timely diagnosis and treatment of older adults. Point-of-care technology, such as focused lung ultrasound scan and bedside analysis of blood samples (leucocytes with differential count, electrolytes, and creatinine) conducted in the patients' home, may support clinical decision-making, and potentially reduce acute hospital admissions. We present the protocol for a randomized controlled trial, which aims at assessing the effect of focused lung ultrasound scan and bedside blood analysis during in-home assessments among older adults with signs of potential acute respiratory disease on hospital admissions.

METHOD

We will use a parallel open-label, individually randomised controlled trial design in an acute community healthcare setting. The trial will initiate on October 2022 and is expected to end one year later. The study population will include older adults (65 + year), with at least one of the following inclusion criteria: Cough, dyspnoea, fever, fall, or rapid functional decline. Expected study sample will comprise 632 participants. Participants in the control group will receive usual care, while the intervention group will undergo extended point-of-care technology (focused lung ultrasound scan and bedside venous blood analysis), in addition to usual care. The primary outcome is acute hospital admission within 30 days follow-up. Secondary outcomes include readmissions, mortality, length of hospital stay, hospital-free days, complications during hospital admission, treatment initiations or changes, functional level, re-referrals to the acute community healthcare service, and contacts to the primary care physician. A tertiary outcome is the diagnostic accuracy of Acute Community Nurses for conducting focused lung ultrasound compared with a specialist. Outcomes will be analysed as intention-to-treat.

DISCUSSION

To our knowledge, this is the first randomised controlled trial examining the effect of extended use of point-of-care technology conducted in an in-home setting. We expect that the results may contribute to the development of new interventions aiming to improve timely diagnostics, treatment decisions, and reduce acute hospital admissions.

TRIAL REGISTRATION

www.

CLINICALTRIALS

org NCT05546073 (Date of registration: September 19th, 2022).