Uncertainty remains over the relationship between blood pressure (BP) variability (BPV), measured in hospital settings, and clinical outcomes following acute ischemic stroke (AIS). We examined the ...association between within-person systolic blood pressure (SBP) variability (SBPV) during hospitalization and readmission-free survival, all-cause readmission, or all-cause mortality 1 year after AIS.
In a cohort of 862 consecutive patients (age mean ± SD 75 ± 15 years, 55% women) with AIS (2005-2018, follow-up through 2019), we measured SBPV as quartiles of standard deviations (SD) and coefficient of variation (CV) from a median of 16 SBP readings obtained throughout hospitalization.
In the cumulative cohort, the measured SD and CV of SBP in mmHg were 16 ± 6 and 10 ± 5, respectively. The hazard ratios (HR) for readmission-free survival between the highest vs. lowest quartiles were 1.44 (95% confidence interval CI 1.04-1.81) for SD and 1.29 (95% CI 0.94-1.78) for CV after adjustment for demographics and comorbidities. Similarly, incident readmission or mortality remained consistent between the highest vs. lowest quartiles of SD and CV (readmission: HR 1.29 95% CI 0.90-1.78 for SD, HR 1.29 95% CI 0.94-1.78 for CV; mortality: HR 1.15 95% CI 0.71-1.87 for SD, HR 0.86 95% CI 0.55-1.36 for CV).
In patients with first AIS, SBPV measured as quartiles of SD or CV based on multiple readings throughout hospitalization has no independent prognostic implications for the readmission-free survival, readmission, or mortality. This underscores the importance of overall patient care rather than a specific focus on BP parameters during hospitalization for AIS.
Objectives
To assess the degree to which self‐reported symptoms predict unplanned readmission or emergency department (ED) care within 30 days of high‐risk, elderly adults enrolled in a ...posthospitalization care transition program (CTP).
Design
Retrospective cohort study.
Setting
Posthospitalization CTP at Mayo Clinic, Rochester, Minnesota, from January 1, 2013, through March 3, 2015.
Participants
Frail, elderly adults (N = 230; mean age 83.5 ± 8.3, 46.5% male).
Measurements
Charlson Comorbidity Index (CCI) and self‐reported symptoms, measured using the Edmonton Symptom Assessment System (ESAS), were ascertained upon CTP enrollment.
Results
Mean CCI was 3.9 ± 2.3. Of 51 participants returning to the hospital within 30 days of discharge, 13 had ED visits, and 38 were readmitted. Age, sex, and CCI were not significantly different between returning and nonreturning participants, but returning participants were significantly more likely to report shortness of breath (P = .004), anxiety (P = .02), depression (P = .02), and drowsiness (P = .01). Overall ESAS score was also a significant predictor of hospital return (P = .01).
Conclusion
Four self‐reported symptoms and overall ESAS score, but not CCI, ascertained after hospital discharge were strong predictors of hospital return within 30 days. Including symptoms in risk stratification of high‐risk elderly adults may help target interventions and reduce readmissions.
National or statewide estimates of excess deaths have limited value to understanding the impact of the COVID-19 pandemic regionally. We assessed excess deaths in a 9-county geographically defined ...population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well-annotated clinical data, and coverage by 2 medical examiner's offices. We compared mortality rates (MRs) per 100,000 person-years in 2020 and 2021 with those in the 2019 reference period and MR ratios (MRRs). In 2020 and 2021, 177 and 219 deaths, respectively, were attributed to COVID-19 (MR = 52 and 66 per 100,000 person-years, respectively). COVID-19 MRs were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared with 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 95% CI, 1.04 to 1.15), with excess deaths in females, older adults, and those with 7 or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared with 2019 (MRR = 1.04 95% CI, 0.99 to 1.10). However, those aged 18 to 39 years (MRR = 1.36 95% CI, 1.03 to 1.80) and those with 0 or 1 chronic condition (MRR = 1.28 95% CI, 1.05 to 1.56) or 7 or more chronic conditions (MRR = 1.09 95% CI, 1.03 to 1.15) had increased mortality compared with 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality.
Aging results in insidious decremental changes in hypothalamic, pituitary and gonadal function. The foregoing three main anatomic loci of control are regulated by intermittent time-delayed signal ...exchange, principally via gonadotropin-releasing hormone (GnRH), luteinizing hormone (LH) and testosterone/estradiol (Te/E
2). A mathematical framework is required to embody these dynamics. The present review highlights integrative adaptations in the aging male hypothalamic–pituitary–gonadal axis, as assessed by recent objective ensemble models of the axis as a whole.
Background The prevalence and clinical impact of chronic conditions (CCs) have increasingly been recognized as an important public health concern. We evaluated the prevalence of coexisting CCs and ...their association with 30-day mortality and readmission in hospitalized patients with stroke and transient ischemic attack (TIA). Methods In a retrospective study of patients aged ≥18 years hospitalized for first-ever stroke and TIA, we assessed the prevalence of coexisting CCs and their predictive value for subsequent 30-day mortality and readmission. Results Study cohort comprised 6771 patients, hospitalized for stroke (n = 4068) and TIA (n = 2703), 51.4% men, with mean age of 68.2 years (standard deviation: ±15.6), mean number of CCs of 2.9 (±1.7), 30-day mortality rate of 8.6% (entire cohort), and 30-day readmission rate of 9.7% (in 2498 patients limited to Olmsted and surrounding counties). In multivariable models, significant predictors of (1) 30-day mortality were coexisting heart failure (HF) (odds ratio OR: 1.45, 95% confidence interval CI: 1.09-1.92), cardiac arrhythmia (OR: 1.74, 95% CI: 1.40-2.17), coronary artery disease (CAD) (OR: 1.64, 95% CI: 1.29-2.08), cancer (OR: 1.67, 95% CI: 1.31-2.14), and diabetes (HR: 1.28, 95% CI: 1.01-1.62); and (2) 30-day readmission (n = 2498) were CAD (OR: 1.50, 95% CI: 1.09-2.07), cancer (OR: 1.46, 95% CI: 1.01-2.10), and arthritis (OR: 1.62, 95% CI: 1.09-2.40). Conclusions In patients hospitalized with stroke and TIA, CCs are highly prevalent and influence 30-day mortality and readmission. Optimal therapeutic and lifestyle interventions for CAD, HF, cardiac arrhythmia, cancer, diabetes, and arthritis may improve early clinical outcome.
Care coordination is a key component of the patient-centered medical home. However, the mechanism for identifying primary care patients who may benefit the most from this model of care is unclear.
To ...evaluate the performance of several risk-adjustment/stratification instruments in predicting healthcare utilization.
Retrospective cohort analysis.
All adults empaneled in 2009 and 2010 (n = 83,187) in a primary care practice were studied. We evaluated 6 models: Adjusted Clinical Groups (ACGs), Hierarchical Condition Categories (HCCs), Elder Risk Assessment, Chronic Comorbidity Count, Charlson Comorbidity Index, and Minnesota Health Care Home Tiering. A seventh model combining Minnesota Tiering with ERA score was also assessed. Logistic regression models using demographic characteristics and diagnoses from 2009 were used to predict healthcare utilization and costs for 2010 with binary outcomes (emergency department ED visits, hospitalizations, 30-day readmissions, and highcost users in the top 10%), using the C statistic and goodness of fit among the top decile.
The ACG model outperformed the others in predicting hospitalizations with a C statistic range of 0.67 (CMS-HCC) to 0.73. In predicting ED visits, the C statistic ranged from 0.58 (CMSHCC) to 0.67 (ACG). When predicting the top 10% highest cost users, the performance of the ACG model was good (area under the curve = 0.81) and superior to the others.
Although ACG models generally performed better in predicting utilization, use of any of these models will help practices implement care coordination more efficiently.
ObjectiveTo examine the effect of HLP, defined as having a pre-existing or a new in-hospital diagnosis based on low density lipoprotein cholesterol (LDL-C) level ≥100 mg/dL during index ...hospitalisation or within the preceding 6 months, on all-cause mortality after hospitalisation for acute myocardial infarction (AMI) or acute decompensated heart failure (ADHF) and to determine whether HLP modifies mortality associations of other competing comorbidities. A systematic review and meta-analysis to place the current findings in the context of published literature.DesignRetrospective study, 1:1 propensity-score matching cohorts; a meta-analysis.SettingLarge academic centre, 1996–2015.ParticipantsHospitalised patients with AMI or ADHF.Main outcomes and measuresAll-cause mortality and meta-analysis of relative risks (RR).ResultsUnmatched cohorts: 13 680 patients with AMI (age (mean) 68.5 ± (SD) 13.7 years; 7894 (58%) with HLP) and 9717 patients with ADHF (age, 73.1±13.7 years; 3668 (38%) with HLP). In matched cohorts, the mortality was lower in AMI patients (n=4348 pairs) with HLP versus no HLP, 5.9 versus 8.6/100 person-years of follow-up, respectively (HR 0.76, 95% CI 0.72 to 0.80). A similar mortality reduction occurred in matched ADHF patients (n=2879 pairs) with or without HLP (12.4 vs 16.3 deaths/100 person-years; HR 0.80, 95% CI 0.75 to 0.86). HRs showed modest reductions when HLP occurred concurrently with other comorbidities. Meta-analyses of nine observational studies showed that HLP was associated with a lower mortality at ≥2 years after incident AMI or ADHF (AMI: RR 0.72, 95% CI 0.69 to 0.76; heart failure (HF): RR 0.67, 95% CI 0.55 to 0.81).ConclusionsAmong matched AMI and ADHF cohorts, concurrent HLP, compared with no HLP, was associated with a lower mortality and attenuation of mortality associations with other competing comorbidities. These findings were supported by a systematic review and meta-analysis.
Objective
People living with dementia often have high care needs at the end-of-life. We compared care delivery in the last year of life for people living with dementia in the community (home or ...assisted living facilities ALFs) versus those in skilled nursing facilities (SNFs).
Methods
A retrospective study was performed of older adults with a dementia diagnosis who died in the community or SNFs from 2013 through 2018. Primary outcomes were numbers of hospitalizations and emergency department visits in the last year of life. Secondary outcomes were completed advance care plans, hospice enrollment, time in hospice, practitioner visits, and intensive care unit admissions.
Results
Of 1203 older adults with dementia, 622 (51.7%) lived at home/ALFs; 581 (48.3%) lived in SNFs. At least 1 hospitalization was recorded for 70.7% living at home/ALFs versus 50.8% in SNFs (P < .001), similar to percentages of emergency department visits (80.2% vs 58.0% of the home/ALF and SNF groups, P < .001). SNF residents had more practitioner visits than home/ALF residents: median (IQR), 9.0 (6.0-12.0) versus 5.0 (3.0-9.0; P < .001). No advance care plan was documented for 12.2% (n = 76) of the home/ALF group versus 4.6% (n = 27) of the SNF group (P < .001). Nearly 57% of SNF residents were enrolled in hospice versus 68.3% at home/ALFs (P < .001). The median time in hospice was 26.5 days in SNFs versus 30.0 days at home/ALFs (P = .67).
Conclusions
Older adults with dementia frequently receive acute care in their last year of life. Hospice care was more common for home/ALF residents. Time in hospice was short.
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