Short- and long-term mortality following acute medical admission.

BACKGROUND: Short-term in-hospital mortality following acute medical admission has been widely investigated. Longer term mortality, particularly out-of-hospital mortality, has been less well studied. AIM: The aim of this study is to evaluate short- and long-term mortality, and predictors of such, following acute medical admission. DESIGN: Retrospective database study. METHODS: We evaluated all acute medical admissions to our institution over 10 years (2002-11) with a minimum of a further 10 years follow-up to 2021 using the Irish National Death Register. Predictors of 30-day in-hospital and long-term mortality were analysed with logistic and Cox regression, with loss of life years estimated. RESULTS: The 2002-11 cohort consisted of 62 184 admissions in 35 140 patients. 30-Day in hospital mortality (n = 3646) per patient was 10.4% and per admission was 5.9%. There were an additional 11 440 longer-term deaths by 2021-total mortality was 15 086 (42.9%). Deaths post hospital discharge had median age at admission of 75.4 years [interquartile range (IQR) 63.7, 82.8] and died at median age of 80 years (IQR 69, 87). The half-life of survival following admission was 195 months-representing a short fall of 8 life years (32.9%) compared with the projected population reference of 24.3 years. Age [odds ratio (OR) 1.73 (95% confidence interval (CI) 1.64, 1.81)], acute illness severity score [OR 1.39 (95% CI 1.36, 1.43)] and comorbidity score [OR 1.09 (95% CI 1.08, 1.10)] predicted long-term mortality. CONCLUSION: Similar factors influence both short- and long-term mortality following acute medical admission, the magnitude of effect is attenuated over time.

Outcomes for Emergency Department Final Diagnosis of Non-specific Complaint compared to Dyspnoea and Pain.

AIM: To compare outcomes in Emergency Department (ED) final diagnoses of (non-specific complaint) NSC, dyspnoea and pain. METHODS: We studied all ED final diagnoses of NSC, dyspnoea, and pain over 6 years (2015-2020). Multivariable logistic regression was performed. RESULTS: There were 49,965 admissions. 30-day in-hospital mortality was significantly lower for pain, 3.0% (95%CI 2.4%, 3.6%), compared to NSC, 4.2% (95%CI 3.8%, 4.7%), and dyspnoea, 4.6% (95%CI 4.2%, 5.0%). NSC did not predict 30-day in-hospital mortality- univariate OR 1.05 (95%CI 0.93, 1.19), multivariable OR 1.07 (95%CI 0.93, 1.23). Comorbidity and Acute Illness Severity Scores demonstrated a curvilinear relationship with 30-day in-hospital mortality. CONCLUSION: An ED final diagnosis of NSC did not predict 30-day in-hospital mortality.

Synthesising existing research on complicated grief in intellectual disability: findings from a systematic review.

BACKGROUND: Complicated grief has been identified as a phenomenon in the general population, and there is an increasing body of research investigating complicated grief in people with intellectual disability. The aim of this study is to synthesise this existing knowledge from research published between 1999 and 2022. METHODS: A structured systematic review using PRISMA guidelines was conducted, which searched three commonly used databases (Medline, PsycINFO and CINAHL) for research on the topic of bereavement and intellectual disability. The articles identified in this search were screened to identify those that addressed the issue of 'complicated grief', with all abstracts and subsequent full texts reviewed by two researchers. RESULTS: In total, 179 abstracts were initially identified, with 34 articles eligible for full text screening and 18 papers reaching criteria for inclusion. Data relating to the studies' objectives were extracted under the headings of definition, defining principles, signs and symptoms, risk factors and treatments for complicated grief in intellectual disability. Thematic analysis of the extracted data was performed to identify key themes. CONCLUSIONS: This review highlights that people with intellectual disability are likely to experience complicated grief reactions and that complicated grief is both underestimated and a clinically significant condition for people with intellectual disability. Future research should work to clarify diagnostic criteria and identify appropriate interventions.

Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine.

BACKGROUND: It is clear that individual outcomes of spine surgery can be quite heterogeneous. When consenting a patient for surgery, it is important to be able to offer an individualized prediction regarding the likely outcome. This study used a comprehensive set of data collected over 12 years in an in-house registry to develop a parsimonious model to predict the multidimensional outcome of patients undergoing surgery for degenerative pathologies of the thoracic, lumbar or cervical spine. METHODS: Data from 8374 patients (mean age 63.9 (14.9-96.3) y, 53.4% female) were used to develop a model to predict the 12-month scores for the Core Outcome Measures Index (COMI) and its subdomain scores. The data were split 80:20 into a training and test set. The top predictors were selected by applying recursive feature elimination based on LASSO cross validation models. Based on the 111 top predictors (contained within 20 variables), Ridge cross validation models were trained, validated, and tested for each of 9 outcome domains, for patients with either "Back" (thoracic/lumbar spine) or "Neck" (cervical spine) problems (total 18 models). RESULTS: Among the strongest outcome predictors in most models were: preoperative scores for almost all COMI items (especially axial pain (back or neck) and peripheral pain (leg/buttock or arm/shoulder)), catastrophizing, fear avoidance beliefs, comorbidity, age, BMI, nationality, previous spine surgery, type and spinal level of intervention, number of affected levels, and surgeon seniority. The R2 of the models on the validation/test sets averaged 0.16/0.13. A preliminary online tool was programmed to present the predicted outcomes for individual patients, based on their presenting characteristics. https://linkup.kws.ch/prognostictool . CONCLUSION: The models provided estimates to enable a bespoke prediction of the outcome of surgery for individual patients with varying degenerative pathologies and baseline characteristics. The models form the basis of a simple, freely-available online prognostic tool developed to improve access to and usability of prognostic information in clinical practice. It is hoped that, following confirmation of its validity and practical utility, the tool will ultimately serve to facilitate decision-making and the management of patients' expectations.

The 'Unwell' clinical presentation- an opportunity for admission avoidance?

BACKGROUND: An 'unwell' patient is a common presentation. METHODS: We studied all ED 'unwell' admissions over 6 years, assessing factors influencing mortality with logistic regression. RESULTS: From 49,965 admissions, the ED diagnosis was 'unwell' in 3650 (7.3%). 'Unwell' presentations were older and had longer length of stay. Mortality was not different 4.2% vs 4.6 % (p=0.28). Respiratory patients and those >=70 years had increased mortality, 8.3% (95%CI: 5.9%, 10.6%) and 7.1% (5.7%, 8.4%) respectively. Being unwell predicted a better outcome - univariate OR 0.35 (95%CI: 0.24, 0.52), multivariable OR 0.68 (95%CI: 0.44, 1.03). CONCLUSION: A diagnosis of 'unwell' applied to a heterogenous group; clinical trajectories and outcomes were sufficiently different to preclude targeted admission avoidance as a strategy.

Blood Culture and Troponin Testing in Suspected Bacteraemic Admissions - Example of Risk Stratification Based on Clinical Testing.

AIM: To investigate the clinical predictive value of troponin (hscTnT) and blood culture testing. METHODS: We examined all medical admissions from 2011-2020. Prediction of 30-day in-hospital mortality, dependent on blood culture and hscTnT requests/results, was evaluated using multiple variable logistic regression. Length of stay was related to utilization of procedures/services with truncated Poisson regression. RESULTS: There were 77,566 admissions in 42,325 patients. With both blood cultures and hscTnT requested, 30-day in-hospital mortality increased to 20.9% (95%CI: 19.7, 22.1) vs 8.9% (95%CI: 8.5, 9.4) for blood cultures alone and 2.3% (95%CI: 2.2, 2.4) with neither. Blood culture 3.93 (95%CI: 3.50, 4.42) or hsTnT requests 4.58 (95%CI: 4.10, 5.14) were prognostic. CONCLUSION: Blood culture and hscTnT requests and results predict worse outcomes.

Limited Prognostic Utility of a Simplified Vital Sign Based Risk Calculator in Acute Medical Admissions.

BACKGROUND: Accurate efficient prognostication in acute medical admissions remains challenging. METHODS: We constructed a Vital Sign based Risk Calculator using vital parameters and Major Disease Categories to predict 30-day in-hospital mortality using a multivariable fractional polynomial model. RESULTS: We evaluated 113,807 admissions in 58,126 patients. The Vital Sign based Risk Calculator predicted 30-day inhospital mortality to increase from 2 points - 3.6% (95%CI 3.4, 3.7) to 12 points - 14.8% (95%CI 14.0, 15.7). AUROC was 0.74 (95%CI 0.72, 0.74). The addition of illness severity and comorbidity data improved AUROC to 0.90 (95%CI 0.89, 0.90). CONCLUSION: The Vital Sign based Risk Calculator is limited by its simplicity; inclusion of illness severity and comorbidity data improve prediction.

Prognostic Value Of Blood Cultures as an Illness Severity Marker In Emergency Medical Admissions.

BACKGROUND: Positive blood cultures predict mortality. The prognostic value of blood culture performance itself has not been fully defined. METHODS: We evaluated medical admissions from 2002-2017. We defined blood culture category as 1) no culture 2) negative culture 3) positive culture. We employed a multivariable logistic regression model to evaluate outcomes. RESULTS: We evaluated 78,568 blood cultures in 106,586 admissions. 30-day in-hospital mortality for no culture was 2.8% (95%CI 2.7, 2.9), culture negative 8.9% (95%CI 8.5, 9.3) and culture positive 16.7% (95%CI 15.5, 17.9). There was significant interaction between blood culture category and illness severity, OR 1.06 (95%CI 1.05, 1.08), and comorbidity, OR 1.09 (95%CI 1.09, 1.10). CONCLUSION: Performance and results of blood cultures are independently associated with increased mortality.

Air pollution and comorbidity burden influencing acute hospital mortality outcomes in a large academic teaching hospital in Dublin, Ireland: a semi-ecologic analysis.

OBJECTIVE: To investigate the extent to which air pollution interacts with comorbidity in determining mortality outcomes of emergency medical admissions. STUDY DESIGN: Routinely collected data were used to study all emergency medical admissions to an academic teaching hospital in Dublin, Ireland, from 2002 to 2018. Air pollution was measured by particulate matter with aerodynamic diameter ≤10 µm (PM10) and sulphur dioxide (SO2) levels on the day of admission. Comorbidity Score was measured using a previously derived score. METHODS: A multivariable logistic regression model was used to relate air pollutant levels, Comorbidity Scores, and their interaction to 30-day in-hospital mortality. RESULTS: There were 102,483 admissions in 58,127 patients over 17 years. Both air pollutant levels and Comorbidity Score were associated with 30-day in-hospital mortality. On admission days with PM10 levels above the median, mortality was higher (Odds ratio [OR] 1.09; 95% confidence interval [CI] 1.06, 1.18) at 11.2% (95% CI 10.5, 12) compared with 10.4% (95% CI 10, 10.7) on days when PM10 levels were below the median. On admission days with SO2 levels above the median, mortality was higher (OR 1.13; 95% CI 1.10, 1.16) at 12.2% (95% CI 11.4, 13) compared with 10.7% (95% CI 10.3, 11.1) on days when SO2 levels were below the median. Comorbidity Score was strongly associated with mortality (mortality rate of 8.9% for those with a 6-point score vs mortality rate of 30.3% for those with a 16-point score). There was limited interaction between air pollutant levels and Comorbidity Score. CONCLUSION: Both air pollution levels on the day of admission and Comorbidity Score were associated with 30-day in-hospital mortality. However, there was limited interaction between these two factors.

Socio-Economic Status and MultiOriginal morbidity - Fact or Fiction?

BACKGROUND: Areas of low socio-economic status (SES) have a disproportionate number of emergency medical admissions; we quantitate the profile of multi-morbidity related to SES. METHODS: We developed a logistic multiple variable regression model, based on over 15 years of hospital data, to examine the effect of socio-demography on hospital outcomes. RESULTS: Admissions from low SES cohort were a decade younger, and had a shorter hospital stay, and lower 30-day episode mortality outcome. The number of morbidities was equivalent between groups, but the more disadvantaged were more likely to have a respiratory diagnosis or diabetes. CONCLUSION: Low SES emergency admissions present > 10 yr. earlier than the high SES population; their equivalent multimorbidity, despite a lower age, could reflect accelerated disease progression.

The Problems with Risk Prediction during an Emergency Medical Admission Using Laboratory Data - Evidence from Potassium.

BACKGROUND: The prediction of clinical outcomes using biochemical markers is an important tool. METHODS: We calculated a risk score for all emergency admissions 2002-2017. We related potassium and mortality in a multivariable fractional polynomial model. We investigated the potassium distribution and relationship of potassium to mortality over time. RESULTS: There were 106,586 admissions in 54,928 patients. Mortality was higher for those with an admission potassium above the median - 6.1% vs 4.6% (p<0.001), OR 1.07 (95%CI: 1.06, 1.09). There was a progressive increase in mortality from the lowest - 8.9% (95%CI: 8.3%, 9.4%) to highest potassium decile - 14.2% (95%CI: 13.5%, 14.8%). The frequency of admission hypokalaemia and the mortality at any given potassium decreased over time. CONCLUSION: Admission potassium predicts mortality.

Mortality outcomes and emergency department wait times - the paradox in the capacity limited sytem.

BACKGROUND: There is concern that undue ED wait times may result in adverse outcomes. METHODS: We studied 30-day in-hospital mortality (2002-2017) for all medical admissions (106,586 episodes; 54,928 patients) focusing on clinical risk profile. RESULTS: Comparing 2002-09 vs. 2010-17, median ED waits > 6 hours (hr) increased 10h (95% CI: 8,13) to 15h (95% CI: 9,19). 30-day mortality declined 6.2% to 4.9%- (RRR- 20.8%/ NNT- 78). 30-day-mortality by ED wait: - < 4hr 6.6% (95% CI: 6.3%, 6.9%), 4-8hr 4.8% (95% CI: 4.6%, 5.0%), 8-12hr 4.3% (95% CI: 4.1%, 4.5%) or >=12hr 4.2% (95% CI: 3.9%, 4.5%). CONCLUSION: Admissions with shorter waits are overrepresented with high clinical acuity. Higher Risk Score patient with extended wait times had worse clinical outcomes.

Improved mortality outcomes over time for weekend emergency medical admissions.

BACKGROUND: Multiple studies have suggested an association between weekend hospital admissions and mortality. These have been limited by potential residual confounders and a lack of explanation of causation. AIM: We previously attributed adverse weekend outcomes to higher acuity; we have re-examined this question for all emergency medical admissions to our institution from 2002 to 2014. METHODS: We divided admissions by a weekday or weekend (Friday to Sunday) hospital arrival. We utilised a multivariate logistic regression model, to determine whether the latter was independently predictive of 30-day in-hospital mortality. RESULTS: There were 82,368 admissions in 44,628 patients over the 13-year period. Of admissions, 37.4% occurred at the weekend. The Acute Illness Severity Score, the Charlson Co-morbidity Index and the Chronic Disabling Disease Score were similar by a weekday or weekend admission. The multivariable logistic regression showed no increase in 30-day in-hospital mortality for weekend admissions, odds ratio 1.07 (95% confidence interval 0.98 to 1.16) (p = 0.11). Since the inception of the AMAU, the per patient mortality for a weekend admission has declined from 13.5% in 2002 to 4.4% in 2014. This represents a relative risk reduction of 67.9% with a number needed to treat of 10.8. Outcomes improved similarly for weekday and weekend admissions. CONCLUSION: No increase in 30-day in-hospital mortality for weekend admissions was found in this study. There has been a substantial reduction in mortality for both weekday and weekend admissions over time.

Dural sac cross-sectional area and morphological grade show significant associations with patient-rated outcome of surgery for lumbar central spinal stenosis.

PURPOSE: Lumbar central spinal stenosis (LSS) is one of the most common reasons for spine surgery in the elderly patient. Magnetic resonance imaging (MRI) represents the gold standard for the assessment of LSS and can be used to obtain quantitative measures of the dural sac cross-sectional area (DCSA) or qualitative measures (morphological grades A-D) of the rootlet/cerebrospinal fluid ratio. This study investigated the intercorrelation between these two MRI evaluation methods and explored their respective relationships with the patient baseline clinical status and outcome 12 months after surgery. METHODS: This was a retrospective analysis of prospectively collected data from 157 patients (88 male, 69 female; age 72 ± 7 years) who were undergoing first-time surgery for LSS. Patients with foraminal or isolated lateral stenosis were excluded. The Core Outcome Measures Index (COMI) was completed before and 12 months after surgery. Preoperative T2 axial MRIs were blinded and independently evaluated for DCSA and morphological grade. Spearman rank correlation coefficients described the relationship between the two MRI measures of stenosis severity and between each of these and the COMI baseline and change-scores (pre to 12 months' postop). Multiple logistic regression analysis (controlling for baseline COMI, age, gender, number of operated levels, health insurance status) was used to analyse the influence of stenosis severity on the achievement of the minimum clinically important change (MCIC) score for COMI and on global treatment outcome (GTO). RESULTS: There was a correlation of ρ = -0.69 (p < 0.001) between DCSA and morphological grade. There was no significant correlation between COMI baseline scores and either DCSA or morphological grades (p > 0.85). However, logistic regression revealed significant (p < 0.05) associations between stenosis ratings and 12-month outcome, whereby patients with more severe stenosis (as measured using either of the methods) benefited more from the surgery. Patients with a DCSA <75 mm2 or morphological grade D had a 4-13-fold greater odds of achieving the MCIC for COMI or a "good" GTO, compared with patients in the least severe categories of stenosis. CONCLUSIONS: Postoperative outcome was clearly related to the degree of preoperative radiological LSS. The two MRI methods appeared to deliver similar information, as given by the relatively strong correlation between them and their comparable performance in relation to baseline and 12-month outcomes. However, the qualitative morphological grading can be performed in an instant, without measurement tools, and does not deliver less clinically useful information than the more complex and time-consuming measures; as such, it may represent the preferred method in the clinical routine for assessing the extent of radiological stenosis and the likelihood of a positive outcome after decompression.

Survival analysis of weekend emergency medical admissions.

BACKGROUND: We previously reported weekend emergency admissions to have a higher mortality; we have now examined the time profile of deaths, by weekday or weekend admission, in all emergency medical patients admitted between 2002 and 2014. METHODS: We divided admissions by a weekday or weekend (After 17.00 Friday-Sunday) hospital arrival. We examined survival following an admission using Cox proportional hazard models and Kaplan-Meier time to event analysis. RESULTS: In total 82 368 admissions were recorded in 44, 628 patients. Weekend admissions had an increased mortality of 5.0% (95% CI 4.7, 5.4) compared with weekday admissions of 4.5% (95% CI 4.3, 4.7) ( P = 0.007). The univariate adjusted Odds Ratio (OR) of death for a weekend admission was significantly increased OR = 1.15 (95% CI 1.05, 1.24) ( P = 0.001). Mortality following an admission declined exponentially over time with a long tail, ∼25% of deaths occurred after day 28. Only 11.4% of deaths occurred on the weekend of the admission. Survival curves showed no mortality difference at 28 days ( P = 0.21) but a difference at 90 days ( P = 0.05). The higher mortality for a weekend admission was attributable to late deaths in the cohort with an extended stay; compared with weekday, these weekend admissions were more likely to be older and have greater co-morbidity. CONCLUSION: Survival rates following a weekend or weekday admission were similar out to 28 days. The higher overall mortality for weekend admissions is due to divergence in survival between 28 and 90 days. Most deaths in weekend admissions occurred when the hospital was fully staffed.

Interaction of Broadband Access and Education Levels in Determining the Rate of Emergency Medical Admissions.

Background We examined the effect of broadband access, educational status and their interaction on local population health. Methods We calculated the annual admission incidence rates for each small area population unit within our hospital catchment, relating quintiles of broadband access to two groups a) full time education to primary level (less than or equal to 15 years) and b) full time education to tertiary level (>18 years). Univariate and multivariable risk estimates were calculated, using truncated Poisson regression. Results 82,368 admissions in 44,628 patients were included. Broadband access was a linear predictor of the admission incidence rate with decreases from Q1 (least access) 50.8 (95%CI 30.2 to 71.4) to Q5 (highest access) 17.9 (95%CI 13.4 to 22.4). Areas with greater numbers educated only to primary level were more influenced by broadband access. Conclusion Broadband access is a predictor of the emergency medical admission rate; this effect is modulated by the baseline education level.

Age and the economics of an emergency medical admission-what factors determine costs?

BACKGROUND: The ageing of the population may be anticipated to increase demand on hospital resources. We have investigated the relationship between hospital episode costs and age profile in a single centre. METHODS: All Emergency Medical admissions (33 732 episodes) to an Irish hospital over a 6-year period, categorized into three age groups, were evaluated against total hospital episode costs. Univariate and adjusted incidence rate ratios (IRRs) were calculated using zero truncated Poisson regression. RESULTS: The total hospital episode cost increased with age ( P < 0.001). The multi-variable Poisson regression model demonstrated that the most important drivers of overall costs were Acute Illness Severity-IRR 1.36 (95% CI: 1.30, 1.41), Sepsis Status -1.46 (95% CI: 1.42, 1.51) and Chronic Disabling Disease Score -1.25 (95% CI: 1.22, 1.27) and the Age Group as exemplified for those 85 years IRR 1.23 (95% CI: 1.15, 1.32). CONCLUSION: Total hospital episode costs are a product of clinical complexity with contributions from the Acute Illness Severity, Co-Morbidity, Chronic Disabling Disease Score and Sepsis Status. However age is also an important contributor and an increasing patient age profile will have a predictable impact on total hospital episode costs.

Influence of social deprivation, overcrowding and family structure on emergency medical admission rates.

BACKGROUND: Patients from deprived backgrounds have a higher in-patient mortality following emergency medical admission. AIM: To evaluate the influence of Deprivation Index, overcrowding and family structure on hospital admission rates. DESIGN: Retrospective cohort study. METHODS: All emergency medical admissions from 2002 to 2013 were evaluated. Based on address, each patient was allocated to an electoral division, whose small area population statistics were available from census data. Patients were categorized by quintile of Deprivation Index, overcrowding and family structure, and these were evaluated against hospital admission rate, calculated as rate/1000 population. Univariate and multivariable risk estimates (Odds Ratios or Incidence Rate Ratios) were calculated, using logistic or zero truncated Poisson regression as appropriate. RESULTS: There were 66 861 admissions in 36 214 patients over the 12-year study period. Deprivation Index quintile independently predicted the admission rate, with rates of Q1 12.0 (95% CI 11.8-12.2), Q2 19.5 (95% CI 19.3-19.6), Q3 33.7 (95% CI 33.3-34.0), Q4 31.4 (95% CI 31.2-31.6) and Q5 38.1 (95% CI 37.7-38.5). Similarly the proportions of families with children <15 years old, was an independent predictor of the admission rate with rates of Q1 20.8 (95% CI 20.4-21.1), Q2 23.0 (95% CI 22.7-23.3), Q3 32.2 (95% CI 31.9-32.5), Q4 32.4 (95% CI 32.2-32.7) and Q5 37.2 (95% CI 36.6-37.8). The proportion of families with children ≥15-years old was also predictive but quintile of overcrowding was only predictive in the univarate model. CONCLUSION: Deprivation Index and family structure strongly predict emergency medical hospital admission rates.

Social deprivation and the rate of emergency medical admission for older persons.

BACKGROUND: Deprivation Status increases the annual admission incidence of emergency medical admissions; the extent to which deprivation influences the admission of older persons is less well known. AIM: To examine whether deprivation within a hospital catchment area influences emergency medical admissions for the elderly population. DESIGN: The relationship between Deprivation Status, Dependency Ratio (population proportion of non-working age (<15 or ≥65 years) and age for all emergency admissions (82 368 episodes of 44 628 patients), over a 13-year period, were examined and ranked by quintile. METHODS: Univariate and multi-variable risk estimates (incidence rate ratios) were calculated, using truncated Poisson regression. RESULTS: The Dependency Ratio and the Deprivation index independently predicted the annual incidence rate of medical emergencies; however, when calculated for older persons, the corresponding incidence rate ratios showed a falling trend with increasing Deprivation Status-Q2 0.51 (95% confidence interval [CI]: 0.50, 0.52), Q3 0.59 (95% CI: 0.58, 0.60), Q4 0.51 (95% CI: 0.50, 0.52) and Q5 0.37 (95% CI: 0.36, 0.38). Thus, with increasing Deprivation Status, the proportion of total admission from the ≥65-year cohort fell substantially. CONCLUSION: The admission incidence rate for emergency medical patients is strongly influenced by the catchment area Deprivation Status. However, because of its greater impact on the younger population, increasing deprivation alters the ratio of younger to older persons as a proportion of total emergency admissions.