Non-specific diagnoses are frequent in patients hospitalized after calling 112 and their mortality is high - a register-based Danish cohort study

Deaths Among Ambulance Patients Released from the Emergency Department Within the First 24 Hours With Nonspecific Diagnoses - Expected or Not?

BACKGROUND

Emergency patients are frequently assigned nonspecific diagnoses. Nonspecific diagnoses describe observations or symptoms and are found in chapters R and Z of the International Classification of Diseases, 10th edition (ICD-10). Patients with such diagnoses have relatively low mortality, but due to patient volume, the absolute number of deaths is substantial. However, information on cause of short-term mortality is limited.

OBJECTIVES

To investigate whether death could be expected for ambulance patients brought to the emergency department (ED) after a 1-1-2 call, released with a nonspecific ICD-10 diagnosis within 24 h, and who subsequently died within 30 days.

METHODS

Retrospective medical record review of adult 1-1-2 emergency ambulance patients brought to an ED in the North Denmark Region during 2017-2021. Patients were divided into three categories: unexpected death, expected death (terminal illness), and miscellaneous. Charlson Comorbidity Index (CCI) was assessed.

RESULTS

We included 492 patients. Mortality was distributed as follows: Unexpected death 59.2% (n = 291), expected death (terminal illness) 25.8% (n = 127), and miscellaneous 15.0% (n = 74). Patients who died unexpectedly were old (median age of 82 years) and had CCI 1-2 (58.1%); 43.0% used at least five daily prescription drugs, and they were severely acutely ill upon arrival (24.7% with red triage, 60.1% died within 24 h).

CONCLUSIONS

More than half of ambulance patients released within 24 h from the ED with nonspecific diagnoses, and who subsequently died within 30 days, died unexpectedly. One-fourth died from a pre-existing terminal illness. Patients dying unexpectedly were old, treated with polypharmacy, and often life-threateningly sick at arrival.

Acute patients discharged without an established diagnosis: risk of mortality and readmission of nonspecific diagnoses compared to disease-specific diagnoses

BACKGROUND

Nonspecific discharge diagnoses after acute hospital courses represent patients discharged without an established cause of their complaints. These patients should have a low risk of adverse outcomes as serious conditions should have been ruled out. We aimed to investigate the mortality and readmissions following nonspecific discharge diagnoses compared to disease-specific diagnoses and assessed different nonspecific subgroups.

METHODS

Register-based cohort study including hospital courses beginning in emergency departments across 3 regions of Denmark during March 2019-February 2020. We identified nonspecific diagnoses from the R- and Z03-chapter in the ICD-10 classification and excluded injuries, among others-remaining diagnoses were considered disease-specific. Outcomes were 30-day mortality and readmission, the groups were compared by Cox regression hazard ratios (HR), unadjusted and adjusted for socioeconomics, comorbidity, administrative information and laboratory results. We stratified into short (3-<12 h) or lengthier (12-168 h) hospital courses.

RESULTS

We included 192,185 hospital courses where nonspecific discharge diagnoses accounted for 50.7% of short and 25.9% of lengthier discharges. The cumulative risk of mortality for nonspecific vs. disease-specific discharge diagnoses was 0.6% (0.6-0.7%) vs. 0.8% (0.7-0.9%) after short and 1.6% (1.5-1.7%) vs. 2.6% (2.5-2.7%) after lengthier courses with adjusted HRs of 0.97 (0.83-1.13) and 0.94 (0.85-1.05), respectively. The cumulative risk of readmission for nonspecific vs. disease-specific discharge diagnoses was 7.3% (7.1-7.5%) vs. 8.4% (8.2-8.6%) after short and 11.1% (10.8-11.5%) vs. 13.7% (13.4-13.9%) after lengthier courses with adjusted HRs of 0.94 (0.90-0.98) and 0.95 (0.91-0.99), respectively. We identified 50 clinical subgroups of nonspecific diagnoses, of which Abdominal pain (n = 12,462; 17.1%) and Chest pain (n = 9,599; 13.1%) were the most frequent. The subgroups described differences in characteristics with mean age 41.9 to 80.8 years and mean length of stay 7.1 to 59.5 h, and outcomes with < 0.2-8.1% risk of 30-day mortality and 3.5-22.6% risk of 30-day readmission.

CONCLUSIONS

In unadjusted analyses, nonspecific diagnoses had a lower risk of mortality and readmission than disease-specific diagnoses but had a similar risk after adjustments. We identified 509 clinical subgroups of nonspecific diagnoses with vastly different characteristics and prognosis.

Risk stratification tools for patients with syncope in emergency medical services and emergency departments: a scoping review

BACKGROUND

Patients with a syncope constitute a challenge for risk stratification in (prehospital) emergency care. Professionals in EMS and ED need to differentiate the high-risk from the low-risk syncope patient, with limited time and resources. Clinical decision rules (CDRs) are designed to support professionals in risk stratification and clinical decision-making. Current CDRs seem unable to meet the standards to be used in the chain of emergency care. However, the need for a structured approach for syncope patients remains. We aimed to generate a broad overview of the available risk stratification tools and identify key elements, scoring systems and measurement properties of these tools.

METHODS

We performed a scoping review with a literature search in MEDLINE, CINAHL, Pubmed, Embase, Cochrane and Web of Science from January 2010 to May 2022. Study selection was done by two researchers independently and was supervised by a third researcher. Data extraction was performed through a data extraction form, and data were summarised through descriptive synthesis. A quality assessment of included studies was performed using a generic quality assessment tool for quantitative research and the AMSTAR-2 for systematic reviews.

RESULTS

The literature search identified 5385 unique studies; 38 were included in the review. We discovered 19 risk stratification tools, one of which was established in EMS patient care. One-third of risk stratification tools have been validated. Two main approaches for the application of the tools were identified. Elements of the tools were categorised in history taking, physical examination, electrocardiogram, additional examinations and other variables. Evaluation of measurement properties showed that negative and positive predictive value was used in half of the studies to assess the accuracy of tools.

CONCLUSION

A total of 19 risk stratification tools for syncope patients were identified. They were primarily established in ED patient care; most are not validated properly. Key elements in the risk stratification related to a potential cardiac problem as cause for the syncope. These insights provide directions for the key elements of a risk stratification tool and for a more advanced process to validate risk stratification tools.

Prehospital Release of Patients After Treatment in an Anesthesiologist-Staffed Mobile Emergency Care Unit

IMPORTANCE

Prehospital treatment and release of patients may reduce unnecessary transports to the hospital and may improve patient satisfaction. However, the safety of patients should be paramount.

OBJECTIVE

To determine the extent of unplanned emergency department (ED) contacts, short-term mortality, and diagnostic patterns in patients treated and released by a prehospital anesthesiologist supervising a mobile emergency care unit (MECU).

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used a manual review of prehospital and in-hospital medical records to investigate all living patients who were treated and released by an MECU in Odense, Denmark, between January 1, 2011, and December 31, 2020. Patients were followed up for 30 days after initial contact with the prehospital service.

MAIN OUTCOMES AND MEASURES

Primary outcome measures included unplanned contacts with the emergency department less than 48 hours after prehospital treatment and prehospital assigned diagnosis. Secondary outcomes consisted of mortality at 48 hours and 7 and 30 days.

RESULTS

A total of 3141 patients were identified; 384 were excluded and 2757 were included in the analysis. The median patient age was 40 (IQR, 14-66) years; 1296 (47.0%) were female and 1461 (53.0%) were male. Two hundred thirty-nine patients (8.7% [95% CI, 7.6%-9.8%]) had unplanned contact with the ED within 48 hours; this rate was doubled for patients with respiratory diseases (37 of 248 [14.9% (95% CI, 10.7%-20.0%)]). Fifty-nine of 60 patients who died within 48 hours of release had terminal illness. Excluding these patients, the mortality rates were 0.04% at 48 hours, 0.8% at 7 days, and 2.4% at 30 days. Two thousand sixty-one patients (74.8%) had primarily nondefinitive observational diagnoses.

CONCLUSIONS AND RELEVANCE

The findings of this cohort study suggest that prehospital treatment and subsequent release at the scene is safe. One patient in 12 attended the ED within the ensuing 48 hours. However, for patients with respiratory diseases, this rate was doubled. Hospital admission could be avoided for some patients in the end stage of a terminal illness.

Using machine learning to predict subsequent events after EMS non-conveyance decisions

Background

Predictors of subsequent events after Emergency Medical Services (EMS) non-conveyance decisions are still unclear, though patient safety is the priority in prehospital emergency care. The aim of this study was to find out whether machine learning can be used in this context and to identify the predictors of subsequent events based on narrative texts of electronic patient care records (ePCR).

Methods

This was a prospective cohort study of EMS patients in Finland. The data was collected from three different regions between June 1 and November 30, 2018. Machine learning, in form of text classification, and manual evaluation were used to predict subsequent events from the clinical notes after a non-conveyance mission.

Results

FastText-model (AUC 0.654) performed best in prediction of subsequent events after EMS non-conveyance missions (n = 11,846). The model and manual analyses showed that many of the subsequent events were planned before, EMS guided the patients to visit primary health care facilities or ED next or following days after non-conveyance. The most frequent signs and symptoms as subsequent event predictors were musculoskeletal-, infection-related and non-specific complaints. 1 in 5 the EMS documentation was inadequate and many of these led to a subsequent event.

Conclusion

Machine learning can be used to predict subsequent events after EMS non-conveyance missions. From the patient safety perspective, it is notable that subsequent event does not necessarily mean that patient safety is compromised. There were a number of subsequent visits to primary health care or EDs, which were planned before by EMS. This demonstrates the appropriate use of limited resources to avoid unnecessary conveyance to the ED. However, further studies are needed without planned subsequent events to find out the harmful subsequent events, where EMS non-conveyance puts patient safety at risk.

EMS non-conveyance: A safe practice to decrease ED crowding or a threat to patient safety?

BACKGROUND

The safety of the Emergency Medical Service's (EMS's) non-conveyance decision was evaluated by EMS re-contacts, primary health care or emergency department (ED) visits, and hospitalization within 48 h. The secondary outcome was 28-day mortality.

METHODS

This cohort study used prospectively collected data on non-conveyed EMS patients from three different regions in Finland between June 1 and November 30, 2018. The Adjusted International Classification of Primary Care (ICPC2) as the reason for care was compared to hospital discharge diagnoses (ICD10). Multivariable logistic regressions were used to determine factors that were independently associated with adverse outcomes. Results are presented with adjusted odds ratios (aORs) together with 95% confidence intervals (CIs). Data regarding deceased patients were reviewed by the study group.

RESULTS

Of the non-conveyed EMS patients (n = 11,861), 6.3% re-contacted the EMS, 8.3% attended a primary health care facility, 4.2% went to the ED, 1.6% were hospitalized, and 0.1% died 0-24 h after the EMS mission. The 0-24 h adverse event rate was higher than 24-48 h. After non-conveyance, 32 (0.3%) patients were admitted to an intensive care unit within 24 h. Primary non-urgent EMS mission (aOR 1.49; 95% CI 1.25 to 1.77), EMS arrival at night (aOR 1.82; 95% CI 1.58 to 2.09), ALS unit type vs BLS (aOR 1.43; 95% CI 1.16 to 1.77), rural area (aOR 1.74; 95% CI 1.51 to 1.99), and older patient age (aOR 1.41; 95% CI 1.20 to 1.66) were associated with subsequent primary health care visits (0-24 h).

CONCLUSIONS

Four in five non-conveyed patients did not have any re-contact in follow-up period. EMS non-conveyance seems to be a relatively safe method of focusing ED resources and avoiding ED crowding.