Unplanned Transfers Research Articles

Unplanned transfer of pediatric patients from the general ward to the intensive care unit.

Introduction. During hospitalization, patients may develop significant clinical deterioration and require unplanned admission to the pediatric intensive care unit (PICU). This may result in increased morbidity and mortality. These events are often preceded by a deterioration phase that may go unnoticed. Objective. To determine the frequency, analyze the causes, and describe the clinical characteristics and outcomes of unplanned transfers of hospitalized pediatric patients from the general pediatric ward (GPW) to the PICU, and analyze the differences between urgent and emergent transfers. Population and methods. Prospective, descriptive study; all unplanned transfers from the GPW to the PICU occurring between January 1st , 2014 and December 31st, 2019 were analyzed. Results. There were 212 unplanned transfers (21 transfers per 1000 admissions). An associated comorbidity was present in 76% of transferred patients -being cancer the most frequent one (36%)- and they had been hospitalized for more than 24 hours in the GPW. The most frequent causes of transfer were respiratory distress (43%), sepsis (20%), and neurological/neurosurgical complications (20%). The overall mortality rate was 8.96% (19 patients). Conclusions. The analysis of unplanned transfers is a critical component in the assessment of the quality of care and patient safety of an area, and should be an indicator integrated into the control panel. The interpretation of unplanned transfers as a preventable event is a key paradigm shift.

Prognosis of patients planned and unplanned admission to the intensive care unit after surgery: a comparative study

To compare and analyze the effect of unplanned versus planned admission to the intensive care unit (ICU) on the prognosis of high-risk patients after surgery, so as to provide a clinical evidence for clinical medical staff to evaluate whether the postoperative patients should be transferred to ICU or not after surgery. The clinical data of patients who were transferred to ICU after surgery admitted to the Affiliated Hospital of Guizhou Medical University from January to December in 2021 were retrospectively analyzed, including gender, age, body mass index, past history (whether combined with hypertension, diabetes, pulmonary disease, cardiac disease, renal failure, liver failure, hematologic disorders, tumor, etc.), acute physiology and chronic health evaluation II (APACHE II), elective surgery, pre-operative hospital consultation, length of surgery, worst value of laboratory parameters within 24 hours of ICU admission, need for invasive mechanical ventilation (IMV), duration of IMV, length of ICU stay, total length of hospital stay, ICU mortality, in-hospital mortality, and survival status at 30th day postoperative. The unplanned patients were further divided into the immediate transfer group and delayed transfer group according to the timing of their ICU entrance after surgery, and the prognosis was compared between the two groups. Cox regression analysis was used to find the independent risk factors of 30-day mortality in patients transferred to ICU after surgery. Finally, 377 patients were included in the post-operative admission to the ICU, including 232 in the planned transfer group and 145 in the unplanned transfer group (42 immediate transfers and 103 delayed transfers). Compared to the planned transfer group, patients in the unplanned transfer group had higher peripheral blood white blood cell count (WBC) at the time of transfer to the ICU [×109/L: 10.86 (7.09, 16.68) vs. 10.11 (6.56, 13.27)], longer total length of hospital stay [days: 23.00 (14.00, 34.00) vs. 19.00 (12.00, 29.00)], and 30-day post-operative mortality was higher [29.66% (43/145) vs. 17.24% (40/232)], but haemoglobin (Hb), arterial partial pressure of carbon dioxide (PaCO2), oxygenation index (PaO2/FiO2), and IMV requirement rate were lower [Hb (g/L): 95.00 (78.00, 113.50) vs. 98.00 (85.00, 123.00), PaCO2 (mmHg, 1 mmHg ≈ 0.133 kPa): 36.00 (29.00, 41.50) vs. 39.00 (33.00, 43.00), PaO2/FiO2 (mmHg): 197.00 (137.50, 283.50) vs. 238.00 (178.00, 350.25), IMV requirement rate: 82.76% (120/145) vs. 93.97% (218/232)], all differences were statistically significant (all P < 0.05). Kaplan-Meier survival curve showed that the 30-day cumulative survival rate after surgery was significantly lower in the unplanned transfer group than in the planned transfer group (Log-Rank test: χ2 = 7.659, P = 0.006). Univariate Cox regression analysis showed that unplanned transfer, APACHE II score, whether deeded IMV at transfer, total length of hospital stay, WBC, blood K+, and blood lactic acid (Lac) were associated with 30-day mortality after operation (all P < 0.05). Multifactorial Cox analysis showed that unplanned transfer [hazard ratio (HR) = 2.45, 95% confidence interval (95%CI) was 1.54-3.89, P < 0.001], APACHE II score (HR = 1.03, 95%CI was 1.00-1.07, P = 0.031), the total length of hospital stay (HR = 0.86, 95%CI was 0.83-0.89, P < 0.001), the need for IMV on admission (HR = 4.31, 95%CI was 1.27-14.63, P = 0.019), highest Lac value within 24 hours of transfer to the ICU (HR = 1.17, 95%CI was 1.10-1.24, P < 0.001), and tumor history (HR = 3.12, 95%CI was 1.36-7.13, P = 0.007) were independent risk factors for patient death at 30 days post-operative, and the risk of death was 2.45 times higher in patients unplanned transferred than in those planned transferred. Subgroup analysis showed that patients in the delayed transfer group had significantly longer IMV times than those in the immediate transfer group [hours: 43.00 (11.00, 121.00) vs. 17.50 (2.75, 73.00), P < 0.05]. The 30-day mortality, WBC and total length of hospital stay were higher in patients who were transferred to ICU after surgery, and PaO2/FiO2 was lower. Unplanned transfer, oncology history, use of IMV, APACHE II score, total length of hospital stay, and Lac were independent risk factors for patient death at 30 days postoperatively, and patients with delayed transfer to ICU had longer IMV time.

Critical incidents during anesthesia: prospective audit

BackgroundCritical incident reporting and analysis is one of the key components of patient safety in anesthesiology. The aim of this study was to determine the frequency and characteristics of critical incidents during anesthesia, main causes and factors involved, influence on patient outcomes, prevalence of incident reporting and further analysis.MethodsA multicenter prospective audit was conducted at the clinical departments of the Bogomolets National Medical University during the period from 1 to 2021 to 1 December 2021. 13 hospitals from different Ukrainian regions took part in the study. Anesthesiologists voluntarily submitted critical incident reports into a Google form as they occurred during the working shifts, reporting the details of the incident, and the incident registration routine in their hospital. The study design was approved by the Bogomolets National Medical University (NMU) ethics committee, protocol #148, 07.09.2021.ResultsThe incidence of critical incidents was 9.35 cases per 1000 anesthetic procedures. Most common incidents were related to the respiratory system: difficult airway (26.8%), reintubation (6.4%), oxygen desaturation (13.8%); cardiovascular system: hypotension (14.9%), tachycardia (6.4%), bradycardia (11.7%), hypertension (5.3%), collapse (3.2%); massive hemorrhage (17%). Factors associated with critical incidents were elective surgery (OR 4.8 [3.1–7.5]), age from 45 to 75 years (OR 1.67 [1.1–2.5]), ASA II (OR 38 [13–106]}, III (OR 34 [12–98]) or IV (3.7 [1.2–11]) compared to ASA I; regional anesthesia (OR 0.67 95 CI 0.5–0.9) or general anesthesia (GA) and regional anesthesia combination (OR 0.55 95 CI 0.3–0.9] decreased the risk of incidents compared to GA alone. Procedural sedation was associated with increased risk of a critical incident, compared to GA (OR 0.55 95 CI 0.3–0.9). The incidents occurred most commonly during the maintenance phase (75/113, 40%, OR compared to extubation phase 20 95 CI 8–48) or the induction phases of anesthesia (70/118, 37%, OR compared to extubation phase 18 95 CI 7–43). Among common reasons that could lead to the incident, the physicians have identified: individual patient features (47%), surgical tactics (18%), anesthesia technique (16%) and human factor (12%). The most frequent failings contributing to the incident occurrence were: insufficient preoperative assessment (44%), incorrect interpretation of the patients’ state (33%), faulty manipulation technique (14%), miscommunication with a surgical team (13%) and delay in emergency care (10%). Furthermore, 48% of cases, as judged by participating physicians, were preventable and the consequences of another 18% could be minimized. The consequences of the incidents were insignificant in over a half of the cases, but in 24.5% have led to prolonged hospital stay, in 16% patients required an urgent transfer to the ICU and 3% of patients died during their hospital stay. The majority of the critical incidents (84%) were reported through the hospital reporting system, using mostly paper forms (65%), oral reports (15%) and an electronic database (4%).ConclusionCritical incidents during anesthesia occur rather often, mainly during the induction or maintenance phases of anesthesia, and could lead to prolonged hospital stay, unplanned transfer to the ICU or death. Reporting and further analysis of the incident are crucial, so we should continue to develop the web-based reporting systems on both local and national levels.Study registrationNCT05435287, clinicaltrials.gov, 23/6/2022.

Development of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients.

Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned admission to the intensive care unit (ICU). Classical severity scores have been developed typically using relatively few patient features. Recently, deep learning-based models demonstrated better individualized risk assessments compared to classic risk scores, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-stamped data from electronic health records. We developed a deep learning model based on embedded text from multiple data sources and recurrent neural networks to predict the risk of the composite outcome of unplanned ICU transfer and in-hospital death. The risk was assessed at regular intervals during the admission for different prediction windows. Input data included medical history, biochemical measurements, and clinical notes from a total of 852,620 patients admitted to non-intensive care units in 12 hospitals in Denmark's Capital Region and Region Zealand during 2011-2016 (with a total of 2,241,849 admissions). We subsequently explained the model using the Shapley algorithm, which provides the contribution of each feature to the model outcome. The best model used all data modalities with an assessment rate of 6 hours, a prediction window of 14 days and an area under the receiver operating characteristic curve of 0.898. The discrimination and calibration obtained with this model make it a viable clinical support tool to detect patients at higher risk of clinical deterioration, providing clinicians insights into both actionable and non-actionable patient features.

Risk of in-hospital Deterioration for Children with Single Ventricle Physiology.

Children with single ventricle physiology (SV) are at high risk of in-hospital morbidity and mortality. Identifying children at risk for deterioration may allow for earlier escalation of care and subsequently decreased mortality.We conducted a retrospective chart review of all admissions to the pediatric cardiology non-ICU service from 2014 to 2018 for children < 18 years old. We defined clinical deterioration as unplanned transfer to the ICU or inpatient mortality. We selected children with SV by diagnosis codes and defined infants as children < 1 year old. We compared demographic, vital sign, and lab values between infants with and without a deterioration event. We evaluated vital sign and medical therapy changes before deterioration events.Among infants with SV (129 deterioration events over 225 admissions, overall 25% with hypoplastic left heart syndrome), those who deteriorated were younger (p = 0.001), had lower baseline oxygen saturation (p = 0.022), and higher baseline respiratory rate (p = 0.022), heart rate (p = 0.023), and hematocrit (p = 0.008). Median Duke Pediatric Early Warning Score increased prior to deterioration (p < 0.001). Deterioration was associated with administration of additional oxygen support (p = 0.012), a fluid bolus (p < 0.001), antibiotics (p < 0.001), vasopressor support (p = 0.009), and red blood cell transfusion (p < 0.001).Infants with SV are at high risk for deterioration. Integrating baseline and dynamic patient data from the electronic health record to identify the highest risk patients may allow for earlier detection and intervention to prevent clinical deterioration.

Performance of Early Warning Scoring Systems Regarding Adverse Events of Unanticipated Clinical Deterioration in Complementary and Alternative Medicine Hospitals

PurposeThis study aims to examine the performance of early warning scoring systems regarding adverse events of unanticipated clinical deterioration in complementary and alternative medicine hospitals. MethodsA medical record review of 500 patients from 5-year patient data in two traditional Korean medicine hospitals was conducted. Unanticipated clinical deterioration events included unexpected in-hospital mortality, cardiac arrest, and unplanned transfers to acute-care conventional medicine hospitals. Scores of the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and National Early Warning Score 2 (NEWS2) were calculated. Their performance was evaluated by calculating areas under the receiver-operating characteristic curve for the event occurrence. Multiple logistic regression analyses were performed to determine the factors associated with event occurrence. ResultsThe incidence of unanticipated clinical deterioration events was 1.1% (225/21101). The area under the curve of MEWS, NEWS, and NEWS2 was .68, .72, and .72 at 24 hours before the events, respectively. NEWS and NEWS2, with almost the same performance, were superior to MEWS (p = .009). After adjusting for other variables, patients at low-medium risk (OR = 3.28; 95% CI = 1.02–10.55) and those at medium and high risk (OR = 25.03; 95% CI = 2.78–225.46) on NEWS2 scores were more likely to experience unanticipated clinical deterioration than those at low risk. Other factors associated with the event occurrence included frailty risk scores, clinical worry scores, primary medical diagnosis, prescribed medicine administration, acupuncture treatment, and clinical department. ConclusionsThe three early warning scores demonstrated moderate-to-fair performance for clinical deterioration events. NEWS2 can be used for early identification of patients at high risk of deterioration in complementary and alternative medicine hospitals. Additionally, patient, care, and system factors need to be considered to improve patient safety.

Use of Evidence-Based Vital Signs in Pediatric Early Warning Score to Predict Clinical Deterioration on Acute Care Units.

The pediatric early warning score (PEWS) is a tool used to predict clinical deterioration. Referenced vital sign parameters are based on expert opinion but heart rate and respiratory rate percentiles in hospitalized children have been published. This retrospective case-control study of unplanned intensive care unit (ICU) transfers compares evidence-based vital signs (EBVS) effect on PEWS sensitivity and specificity, determines the impact of age categories on PEWS deterioration prediction, and evaluates whether EBVS PEWS is associated with need for invasive ICU supports. EBVS PEWS improved sensitivity (43%-71% vs 30%-63%) for unplanned transfers with slightly decreased specificity (88%-98% vs 93%-99%). Logistic regression analysis and odds ratios (ORs) demonstrated EBVS PEWS was associated with increased risk for ICU-specific supports (OR = 1.16, 95% confidence interval [CI] = 1.0-1.34, P = .0498). Evidence-based vital signs can improve PEWS sensitivity to identify unplanned ICU transfers and identify patients requiring ICU-specific interventions.

A novel, evidence-based, comprehensive clinical decision support system improves outcomes for patients with traumatic rib fractures.

Traumatic rib fractures are associated with high morbidity and mortality. Clinical decision support systems (CDSS) have been shown to improve adherence to evidence-based (EB) practice and improve clinical outcomes. The objective of this study was to investigate if a rib fracture CDSS reduced hospital length of stay (LOS), 90-day and 1-year mortality, unplanned ICU transfer, and the need for mechanical ventilation. The independent association of two process measures, an admission EB order set and a pain-inspiratory-cough score early warning system, with LOS were investigated. The CDSS was scaled across nine US trauma centers. Following multiple imputation, multivariable regression models were fit to evaluate the association of the CDSS on primary and secondary outcomes. As a sensitivity analysis, propensity score matching was also performed to confirm regression findings. Overall, 3,279 patients met inclusion criteria. Rates of EB practices increased following implementation. On risk-adjusted analysis, in-hospital LOS preintervention versus postintervention was unchanged (incidence rate ratio [IRR], 1.06; 95% confidence interval [CI], 0.97-1.15, p = 0.2) but unplanned transfer to the ICU was reduced (odds ratio, 0.28; 95% CI, 0.09-0.84, p = 0.024), as was 1-year mortality (hazard ratio, 0.6; 95% CI, 0.4-0.89, p = 0.01). Provider utilization of the admission order bundle was 45.3%. Utilization was associated with significantly reduced LOS (IRR, 0.87; 95% CI, 0.77-0.98; p = 0.019). The early warning system triggered on 34.4% of patients; however, was not associated with a significant reduction in hospital LOS (IRR, 0.76; 95% CI, 0.55-1.06; p = 0.1). A novel, user-centered, comprehensive CDSS improves adherence to EB practice and is associated with a significant reduction in unplanned ICU admissions and possibly mortality, but not hospital LOS. Therapeutic/Care Management; Level III.

The Effect of Continuous Versus Periodic Vital Sign Monitoring on Disease Severity of Patients with an Unplanned ICU Transfer

Continuous vital sign monitoring (CM) may detect ward patient’s deterioration earlier than periodic monitoring. This could result in timely ICU transfers or in a transfer delay due to misperceived higher level of care on the ward. The primary objective of this study was to compare patient’s disease severity upon unplanned ICU transfer, before and after CM implementation. We included a one-year period before and after CM implementation between August 1, 2017 – July 31, 2019. Before implementation, surgical and internal medicine patients’ vital signs were periodically monitored, compared to continuous monitoring with wireless linkage to hospital systems after implementation. In both periods the same early warning score (EWS) protocol was in place. Primary outcome was disease severity scores upon ICU transfer. Secondary outcomes were ICU and hospital length of stay, incidence of mechanical ventilation and ICU mortality. In the two one-year periods 93 and 59 unplanned ICU transfer episodes were included, respectively. Median SOFA (3 (2–6) vs 4 (2–7), p = .574), APACHE II (17 (14–20) vs 16 (14–21), p = .824) and APACHE IV (59 (46–67) vs 50 (36–65), p = .187) were comparable between both periods, as were the median ICU LOS (3.0 (1.7–5.8) vs 3.1 (1.6–6.1), p.962), hospital LOS (23.6 (11.5–38.0) vs 19 (13.9–39.2), p = .880), incidence of mechanical ventilation (28 (47%) vs 22 (54%), p.490), and ICU mortality (11 (13%) vs 10 (19%), p.420). This study shows no difference in disease severity upon unplanned ICU transfer after CM implementation for patients who have deteriorated on the ward.

Status and Risk Factors in Patients Requiring Unplanned Intensive Care Unit Readmission Within 48 Hours: A Retrospective Propensity-Matched Study in China.

This study investigated the current status and related risk factors of 48-hour unplanned return to the intensive care unit (ICU) to reduce the return rate and improve the quality of critical care management. Data were collected from 2365 patients discharged from the comprehensive ICU. Multivariate and 1:1 propensity score matching analyses were performed. Forty patients (1.69%) had unplanned readmission to the ICU within 48 hours after transfer. The primary reason for return was respiratory failure (16 patients, 40%). Furthermore, respiratory failure (odds ratio [OR] = 5.994, p = 0.02) and the number of organ failures (OR = 5.679, p = 0.006) were independent risk factors for unplanned ICU readmission. Receiver operating characteristic curves were drawn for the predictive value of the number of organ injuries during a patient's unplanned transfer to the ICU (area under the curve [AUC] = 0.744, sensitivity = 60%, specificity = 77.5%). The reason for patient transfer and the number of organ injuries during the process were independent risk factors for patients who were critically ill. The number of organs damaged had a predictive value on whether the patient would return to the ICU within 48 hours.

Predicting patient deterioration by nurse intuition: The development and validation of the nurse intuition patient deterioration scale

IntroductionEarly warning scores based on vital signs are used in hospitals to estimate patient deterioration and to initiate an adequate and timely response when necessary. These scores show acceptable performance in predicting patient outcomes. However, they tend to generate many false positives leading to an increased workload in clinical practice. Additionally, nurses feel a tension between the application of an early warning score and their own clinical judgement. Nurse intuition is often included as an extra call criterion next to an early warning score. It is therefore important to investigate its predictive value. The aim of this study was to develop and validate a Nurse Intuition Patient Deterioration Scale (NIPDS). MethodsThe NIPDS was developed using the latest evidence after which relevant items were selected by an expert panel. The scale was tested in a prospective observational study in 2 surgical and 2 medical wards in a Belgian hospital. Data were collected from December 1st, 2019 until March 31st, 2020. A NIPDS registration was done at each patient admission and whenever the attending nurse felt worried. The studied outcomes were urgent physician assistance call, resuscitation team call, patient death, and unplanned transfer to intensive/medium care. Psychometric analyses and scale optimisation were carried out using Rasch modelling techniques. Finally, the scale's accuracy and an optimal threshold were determined. ResultsThe scale item content validity index is 0.88 indicating that the selected items should be included in the instrument according to the expert panel. Item-total score correlation coefficients range between 0.573 (item 9 – pain) and 0.874 (item 6 – behaviour). The Person Separation Index is 0.814 indicating satisfactory discriminatory power. An overall fit of the NIPDS data to the Rasch model was confirmed. Rasch modelling showed that the item ‘pain’ signalled misfit. Furthermore, the person-item map showed disordered items which were corrected in the final model. The AUROC to predict an event within 24 h after registration was 0.957 (95% CI 0.932–0.982; p < 0.001) indicating excellent model performance. DiscussionThe results showed that the NIPDS is a valid and accurate instrument to predict events in surgical and medical patients. It showed better performance compared to an existing score estimating nurse intuition. In practice, the NIPDS could be used by nurses to estimate clinical deterioration in addition to an early warning score. It remains unclear if the combination of NIPDS with an early warning score could reduce workload without losing accuracy and this should be explored in future research. Tweetable abstractNewly developed nurse intuition scale, which uses clinical cues to estimate deterioration in hospitalised patients, is brief and performs well in predicting physician assistance, resuscitation team calls, patient death and unplanned transfer to intensive or medium care.

Evaluating the implementation fidelity to a successful nurse-led model (INTERCARE) which reduced nursing home unplanned hospitalisations

BackgroundImplementation fidelity assesses the degree to which an intervention is delivered as it should be. Fidelity helps to determine if the outcome(s) of an intervention are attributed to the intervention itself or to a failure of its implementation. Little is known about how fidelity impacts the intended outcome(s) and what elements or moderators can affect the fidelity trajectory over time. We exemplify the meaning of implementation fidelity with INTERCARE, a nurse-led care model that was implemented in eleven Swiss nursing homes (NHs) and showed effectiveness in reducing unplanned hospital transfers. INTERCARE comprises six core elements, including advance care planning and tools to support inter- and interprofessional communication, which were introduced with carefully developed implementation strategies.MethodsA mixed-methods convergent/triangulation design was used to investigate the influence of implementation fidelity on unplanned transfers. A fidelity questionnaire measuring the degree of fidelity to INTERCARE’s core components was fielded at four time points in the participating NHs. Two-monthly meetings were conducted with NHs (September 2018-January 2020) and structured notes were used to determine moderators affecting fidelity (e.g., participant responsiveness). We used the fidelity scores and generalized linear mixed models to analyze the quantitative data. The Framework method was used for the qualitative analysis. The quantitative and qualitative findings were integrated using triangulation.ResultsA higher overall fidelity score showed a decreasing rate of unplanned hospital transfers post-intervention (OR: 0.65 (CI = 0.43–0.99), p = 0.047). A higher fidelity score to advance care planning was associated with lower unplanned transfers (OR = 0.24 (CI 0.13–0.44), p = < 0.001) and a lower fidelity score for communication tools (e.g., ISBAR) to higher rates in unplanned transfers (OR = 1.69 (CI 1.30–2.19), p = < 0.003). In-house physicians with a collaborative approach and staff’s perceived need for nurses working in extended roles, were important moderators to achieve and sustain high fidelity.ConclusionImplementation fidelity is challenging to measure and report, especially in complex interventions, yet is crucial to better understand how such interventions may be tailored for scale-up. This study provides both a detailed description of how fidelity can be measured and which ingredients highly contributed to reducing unplanned NH transfers.Trial registrationThe INTERCARE study was registered at clinicaltrials.gov Protocol Record NCT03590470.

Ten-Year Trends and Predictors of Unplanned Hospitalisation in Community-Dwelling Older People Receiving Home-Based Care

Older people prefer to remain living in their own home for as long as possible; however, many require support to do so through health and other care services provided in the home. This study aimed to explore the trends in usage of a home-based care service by older people in metropolitan Melbourne and factors associated with unplanned hospitalisations. This longitudinal study analysed episodes of home-based care for people aged ≥65 years between 2006 and 2015. An episode of care was defined as the period of time during which the home care services were provided to the client. Care episodes culminated in a planned discharge from the service or an unplanned hospitalisation. Descriptive statistics and multivariable logistic regression were utilised to investigate the characteristics associated with unplanned hospitalisations. Utilisation of home-based care services over the 10-year period showed an increasing rate of use by people aged ≥85 years and a reduced usage rate by females aged 70–84 years and males 75–79 years old. Of 170,001 episodes of care, 43,608 (25.7%) resulted in an unplanned hospitalisation. Home-based care delivered to people aged ≥85 years showed an increasing rate of episodes ending in an unplanned transfer to the hospital. Between 2006 and 2015, individuals aged 85–89 years displayed a rate increase of 18.7% in episodes ending in an unplanned hospitalisation; for those aged ≥90 years, the rate rise was 43.6%. Factors associated with an unplanned hospitalisation included advancing age, male gender, living alone, cognitive dysfunction, and the complexity of medical issues. Health policy has focussed on providing services to enable older people to remain in their own home. The increasing rate of unplanned hospitalisations for community-dwellers aged ≥85 years suggests more support is required to enable ageing in place.

Frequency and reasons for unplanned transfer to the primary acute care service of inpatient rehabilitation glioblastoma multiforme patients.

To determine the percentage of and factors associated with unplanned transfer to the acute care service of glioblastoma multiforme acute rehabilitation inpatients. Retrospective review of glioblastoma multiforme acute rehabilitation inpatients admitted 4/1/2016-3/31/2020 at a National Cancer Institute Comprehensive Cancer Center. One hundred thirty-nine consecutive admissions of unique glioblastoma multiforme acute rehabilitation inpatients were analyzed. Fifteen patients (10.7%, 95% confidence interval 6.5-17.1%) were transferred to the acute care service for unplanned reasons. The most common reasons for transfer back were neurosurgical complication 6/15(40%), neurologic decline due to mass effect 4/15(26.7%), and pulmonary embolism 2/15(13.3%). Older age (p = 0.010), infection prior to acute inpatient rehabilitation transfer (p = 0.020), and lower activity measure of post-acute care 6-click basic mobility scores (p = 0.048) were significantly associated with transfer to the acute care service. Patients who transferred to the acute care service had significantly lower overall survival than patients who did not transfer off (log-rank test p = 0.001). Acute inpatient physiatrists should closely monitor patients for neurosurgical and neurologic complications. The variables significantly associated with transfer to the acute care service may help identify patients at increased risk for medical complications who may require closer observation.

Preoperative Comorbidities Associated With Early Mortality in Hip Fracture Patients: A Multicenter Study.

Multiple comorbidities in hip fracture patients are associated with increased mortality and complications. The goal of this study was to characterize the relationship between specific patient factors including comorbidities and outcomes in geriatric hip fractures, including length of stay, unplanned ICU admission, discharge disposition, complications, and mortality. This is a retrospective review of a trauma database from five Level 1 and Level 2 trauma centers of patients with hip fractures of the femoral neck and intertrochanteric region who underwent treatment using hip pinning, hemiarthroplasty, total hip arthroplasty, cephalomedullary nailing, or dynamic hip screw fixation. Mortality was the primary outcome variable (including in-hospital mortality, 30-day mortality, 60-day mortality, and 90-day mortality). Secondary outcome variables included in-hospital adverse events, unplanned transfer to the ICU, postoperative length of stay, and discharge disposition. Regression analyses were used for evaluation of relationships between comorbidities as independent variables and primary and secondary outcomes as dependent variables. Two thousand three hundred patients were included. The mortality was 1.8%, 7.0%, 10.9%, and 14.1% for in-hospital, 30-day, 60-day, and 90-day mortality, respectively. Diabetes and cognitive impairment present on admission were associated with mortality at all-time intervals. COPD was the only comorbidity that signaled in-hospital adverse event with an odds ratio of 1.67 (P = 0.012). No patient factors, time to surgery, or comorbidities signaled unplanned ICU transfer. Patients with renal failure and COPD had longer hospital stays after surgery. Geriatric hip fractures continue to have high short-term morbidity and mortality. Identifying patients with increased odds of early mortality and adverse events can help teams optimize care and outcomes. Patients with diabetes, cognitive impairment, renal failure, and COPD may benefit from continued and improved medical optimization during the perioperative period as well as being more closely managed by a medicine team without delaying time to the operating room.

Factors Associated with Unplanned Transfer of Patients with Brain Tumor from Inpatient Rehabilitation Unit to Primary Acute Care Units.

Inpatient rehabilitation should be assessed to improve each functional domain in patients with brain tumor. However, no previous study has reported risk factors for unplanned transfer of this patient population to primary acute care units during a comprehensive inpatient rehabilitation. The objective of this study was to investigate the percentage of unplanned transfer of brain tumor rehabilitation inpatients to primary acute care units compared with stroke patients and factors associated with such unplanned transfer. Data of 137 patients with brain tumor who were transferred to the department of physical and rehabilitation medicine were retrospectively reviewed. For comparison, data of 438 patients with subacute stroke were also obtained. Included patients were divided into an unplanned transfer group and a control group based on whether they required a transfer to another department for acute care before completing their comprehensive inpatient rehabilitation. Reasons for unplanned transfers were classified based on medical or surgical conditions. The incidence of unplanned transfers to the medical or surgical department was significantly higher in patients with brain tumor (15.3%) than in stroke patients (7.1%) (p < 0.05). Most of unplanned transfers occurred within two weeks of the comprehensive inpatient rehabilitation for patients with brain tumor. There was a significantly higher incidence of unplanned transfers in patients with a primary tumor than in those with a metastatic tumor (15.9% vs. 4.8%, p < 0.05). In addition, the frequency of chemotherapy or radiotherapy was significantly (p < 0.05) higher in the unplanned transfer group than in the control group. The most common cause of an unplanned transfer was a neurologic cause (90.0%) in patients with brain tumor and an infectious disease such as pneumonia (51.6%) in stroke patients. In conclusion, these results demonstrated a higher incidence of unplanned transfers in patients with brain tumor than in stroke patients during intensive inpatient rehabilitation. Proportions of those with neurological problems were relatively higher in patients with brain tumor than in patients with subacute stroke.

EVALUATING IMPLEMENTATION FIDELITY TO A NURSE-LED CARE MODEL IN NURSING HOMES: A MIXED-METHODS STUDY

Abstract Implementation fidelity assesses the degree to which an intervention is delivered as intended. Little is known about how it acts as a moderator between an intervention and its intended outcome(s) and which factors affect the fidelity trajectory over time. We exemplify implementation fidelity in INTERCARE, a nurse-led care model implemented in eleven Swiss nursing homes (NH) successfully decreasing unplanned hospital transfers. A mixed-methods design was used, guided by the Conceptual Framework for Implementation Fidelity. Fidelity to INTERCARE’s core components was measured with 44 self-developed items at 4-time points (baseline, 6, 12 months after intervention start, 9 months post-intervention; fidelity scores were calculated for each component and overall. Structured notes from NH meetings were used to identify moderators affecting the fidelity trajectory over time. Generalized linear mixed models were computed to analyze the quantitative data. Deductive thematic analysis was used for the qualitative analysis. The quantitative and qualitative findings were integrated using triangulation. A higher overall fidelity score showed a decreasing rate of unplanned hospital transfers (OR: 0.65 (CI=0.43-0.99), p=0.047). Higher fidelity score to advance care planning was associated with lower unplanned transfers (OR= 0.24 (CI 0.13-0.44), p= &amp;lt; 0.001) and a lower fidelity score for communication tools (e.g., ISBAR) to higher rates in unplanned transfers (OR= 1.69 (CI 1.30-2.19), p= &amp;lt; 0.003). High implementation fidelity to INTERCARE was necessary to achieve a reduction in unplanned transfers. In-house physicians with a collaborative approach and staff’s perceived need for nurses working in extended roles were important factors for high fidelity.

The application value of the Modified Early Warning Score combined with age and injury site scores in the evaluation of injuries in emergency trauma patients.

To explore the application value of the Modified Early Warning Score (MEWS) combined with age and injury site scores in predicting the criticality of emergency trauma patients. The traditional MEWS was modified by combining it with age and injury site scores to form a new MEWS combined scoring standard. The clinical data were collected from a total of 372 trauma patients from the emergency department of the Nantong First People's Hospital between June and December 2019. A retrospective analysis was conducted, and the patients were scored using the MEWS combined with age and injury site scores. The patients were grouped according to their prognoses and clinical outcomes. A statistical analysis was conducted based on the ranges of the combined scores, and the results of the combined scores of the different groups were compared. Among the 372 patients, the average score was 3.68 ± 1.25 points in the survival group, 8.33 ± 2.24 points in the death within 24 h group, and 8.38 ± 1.51 points in the death within 30 days of hospitalization group, and the differences were statistically significant (p < 0.05). The average score was 2.74 ± 0.69 points in the outpatient treatment group, 4.19 ± 0.72 points in the emergency stay group, 5.40 ± 0.70 points in the specialist inpatient group, 8.71 ± 2.31 points in the ICU group, and 7.82 ± 1.66 points in the specialist unplanned transfer to ICU group, with the differences between the groups being statistically significant (p < 0.05). The average length of hospital stay for patients with a joint score within the range of 6-8 points was 10.86 ± 2.47 days, with a direct ICU admission rate of 22.00% and an unplanned ICU admission rate of 16.00%. Patients with a joint score >8 points had an average length of hospital stay of 27.05 ± 4.85 days, with a direct ICU admission rate of 66.67% and an unplanned ICU admission rate of 33.33%. Age and injury site are important high-risk indicators for trauma assessment, and using them in combination with the MEWS could improve the assessment of emergency patients with trauma, increasing the accuracy of pre-screening triage and reducing rescue time. Therefore, this joint scoring method might be worthy of clinical promotion and application.

Pregnancy and childbirth in women with bleeding disorders: A retrospective cohort study.

Reproductive-age women with bleeding disorders (BDs) are underdiagnosed and understudied, despite their increased risk for adverse health outcomes and pregnancy complications. This study examines pregnancy outcomes and obstetric complications of Utah women with BDs. This retrospective cohort study utilized linked birth records and clinical billing data from two large Utah healthcare systems. Utah residents who had their first birth at>20weeks gestation (2008-2015) and who received non-emergent care within either system before delivery were included (n=61 226). Multivariable logistic regression models were used to examine relationships between BDs and neonatal and obstetric outcomes. A total of 295 women (.48%) were included in the BD study population. Women with BDs had significantly increased odds of preterm birth (aOR 1.85, 95% CI 1.32-2.60), Caesarean delivery (aOR 1.38, 95% CI 1.06-1.79), postpartum blood transfusion (aOR 2.55, 95% CI 1.05-6.22), unplanned postpartum hysterectomy (aOR 33.96, 95% CI 7.30-157.89) and transfer to an intensive care unit (aOR 18.18, 95% CI 7.17-46.08). All of the women with BDs who experienced these serious complications were not diagnosed with a BD until the year of their first birth. Additionally, those with BDs were more likely to experience maternal and infant mortality. Women with BDs had an increased risk for preterm birth, Caesarean delivery, blood transfusion, unplanned hysterectomy, intensive care unit admission, maternal and infant mortality. Those who were not diagnosed with a BD before the year of their first birth were at an increased risk for serious pregnancy complications.

Abstract 12628: Evaluation of Time From Pre-Hospital Symptom Onset to Angiography on Coronary Artery Occlusion Status and Outcomes in Patients Presenting With NSTE-ACS

Introduction: Current guidelines for invasive coronary angiography (ICA) in patients presenting with NSTE-ACS outline two treatment pathways: early (within 24 hours [hrs]); or late (&gt;24 hrs). Time of hospital admission is used as the start time, however, we hypothesize that pre-hospital time of symptom onset may be a more optimal starting time. This study was designed to test optimal symptom onset-to-angiography time (OAT) and its association with the presence of coronary occlusion and adverse outcomes. Methods: Secondary data analysis in NSTE-ACS patients (pre-COVID-19) who underwent ICA. We tested the optimal cutoff point of OAT in classifying coronary occlusion using Youden-index analysis. We tested the association of OAT and in-hospital complication (i.e., myocardial infarction [MI] after admission, unplanned transfer to the cardiac intensive care unit, pulmonary edema, cardiogenic shock, dysrhythmia with intervention) and hospital length of stay [LOS]) using regression models. Results: In 163 patients: 124 (76%) had an occluded artery; 37 (23%) had an in-hospital complication. Overall, the mean OAT was 26±22 hrs (24±22 vs. 31±21, with and without occluded artery, respectively), and the median LOS was 55 hrs. The Youden-index optimum OAT cutoff point was 13.4 hrs. In the two logistic models, the adjusted OAT was associated with the presence of coronary occlusion (Figure A). OAT, as a continuous variable, was associated with LOS ( β =0.64, 95% CI 0.08-1.21, p =0.025), no other in-hospital complications were significant (Figure B). Conclusions: In patients presenting with NSTE-ACS, OAT at both 13.4 and &lt;24 hrs is a significant predictor of the presence of coronary artery occlusion. Every hour of delayed OAT was associated with a prolonged hospital LOS 0.64 hrs (38 min). Symptom onset appears to be an important starting point in determining optimal timing of ICA in patients with NSTE-ACS, but requires further study with a large sample of patients.