Proximal humerus fractures - epidemiology, comparison of mortality rates after surgical versus non-surgical treatment, and analysis of risk factors based on Medicare registry data

AIMS

The optimal choice of management for proximal humerus fractures (PHFs) has been increasingly discussed in the literature, and this work aimed to answer the following questions: 1) what are the incidence rates of PHF in the geriatric population in the USA; 2) what is the mortality rate after PHF in the elderly population, specifically for distinct treatment procedures; and 3) what factors influence the mortality rate?

METHODS

PHFs occurring between 1 January 2009 and 31 December 2019 were identified from the Medicare physician service records. Incidence rates were determined, mortality rates were calculated, and semiparametric Cox regression was applied, incorporating 23 demographic, clinical, and socioeconomic covariates, to compare the mortality risk between treatments.

RESULTS

From 2009 to 2019, the incidence decreased by 11.85% from 300.4 cases/100,000 enrollees to 266.3 cases/100,000 enrollees, although this was not statistically significant (z = -1.47, p = 0.142). In comparison to matched Medicare patients without a PHF, but of the same five-year age group and sex, a mean survival difference of -17.3% was observed. The one-year mortality rate was higher after nonoperative treatment with 16.4% compared to surgical treatment with 9.3% (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.23 to 1.36; p < 0.001) and to shoulder arthroplasty with 7.4% (HR = 1.45, 95% CI 1.33 to 1.58; p < 0.001). Statistically significant mortality risk factors after operative treatment included age older than 75 years, male sex, chronic obstructive pulmonary disease (COPD), cerebrovascular disease, chronic kidney disease, a concomitant fracture, congestive heart failure, and osteoporotic fracture.

CONCLUSION

Mortality risk factors for distinct treatment modes after PHF in elderly patients could be identified, which may guide clinical decision-making.Cite this article: Bone Joint Res 2023;12(2):103-112.

Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort

Proportional hazard Cox regression models are overwhelmingly used for analyzing time-dependent outcomes. Despite their associated hazard ratio is a valuable index for the difference between populations, its strong dependency on the underlying assumptions makes it a source of misinterpretation. Recently, a number of works have dealt with the subtleties and limitations of this interpretation. Besides, a number of alternative indices and different Cox-type models have been proposed. In this work, we use synthetic data, motivated by a real-world problem, for showing the strengths and weaknesses of some of those methods in the analysis of time-dependent outcomes. We use the power of synthetic data for considering observable results but also utopian designs.

Depressive symptoms and the risk of arthritis: A survival analysis using data from the osteoarthritis initiative

BACKGROUND

Some studies investigated the association between depressive symptoms and arthritis; however, no longitudinal studies have documented the relationship between developing depressive symptoms and the risk of arthritis. Therefore, this study evaluated whether the development of depressive symptoms was associated with an elevated risk of arthritis.

MATERIALS AND METHODS

A survival analysis using Cox regression models was applied to osteoarthritis initiative data obtained over 6 years from adults (n = 3,662) aged ≥45 years at baseline. Developing depressive symptoms was defined using the 20-item Center for Epidemiologic Studies Depression scale (cutoff 16 points) between baseline and 1 year. Arthritis was defined answering "yes" to the following self-reported question: "Did the doctor say you developed arthritis since the last clinic visit about 1 year ago?" over the 6-year follow-up period.

RESULTS

The hazard ratios for developing arthritis were 3.51 (95% confidence interval [CI] = 2.32-5.29) and 2.03 (95% CI = 1.45-2.85) for men and women, respectively, as compared to those who did not develop depressive symptoms. There was a significantly (χ ² = 73.672, P < 0.0001) lower survival probability at each time point throughout the study among men and women who developed depressive symptoms.

CONCLUSION

In both men and women, developing depressive symptoms increased the risk of arthritis, and the survival probability decreased at each time point.

Survival Rate and Predictors of Mortality among Hemodialysis Patients in West of Iran, 1996-2015

Background:

Hemodialysis (HD) is one of the treatments provided to end-stage renal disease (ESRD) patients. A few studies have investigated the survival rate of HD patients in Iran. Hence, we decided to investigate the survival rate and it is predictors among Iranian ESRD patients.

Methods:

This is a retrospective cohort study conducted in 165 HD patients in Tuyserkan city (Hamadan province) during 20 years from 1996 to 2015. The checklist used to gather information was comprised of patients’ demographic and clinical information. The analysis was performed using Kaplan–Meier curves, log-rank test, and cox regression model.

Results:

The most prevalent cause of ESRD was reported to be high blood pressure (32.7%). The probability of survival rate at the end of 1^st, 5^th, and 10^th year was 0.65, 0.16, and 0.05, respectively. Results of multivariate cox regression showed that old age, catheter vascular access, and high hemoglobin level have a negative significant effect on survival of HD patients (P < 0.05).

Conclusions:

Overall, the survival of HD patients seems to be low in Tuyserkan as compared to other studies. Age, ESRD cause, vascular access, marital status, and hemoglobin level among other factors are proved to have a significant effect on survival probability.

Factors predicting re-hospitalization for inpatients with bipolar mania--A naturalistic cohort

Patients with bipolar mania often require re-hospitalization, which can be a social-economic burden. This study aimed to investigate the factors affecting time to re-hospitalization for inpatients with acute bipolar mania. In this 6-year naturalistic study we reviewed the charts of inpatients with acute bipolar mania. Potential dichotomous factors including sex, family history of affective disorder, suicide history, and discharge against medical advice, involuntary admission, psychotic features, substance use disorder, and remission at discharge were recorded. Continuous variables including age, age at onset of affective episodes, and number of previous hospitalizations were also recorded. We used survival analysis with a univariate Cox proportional hazard regression model, along with forward multivariate model, to identify the potential factors. Of the 165 patients, 23% achieved symptomatic remission at discharge. In the forward Cox model, psychotic features, discharge against medical advice and higher number of previous hospitalizations were significant risk factors for re-admission. Remission at discharge was significantly associated with preventing re-hospitalization. We identified several predictors for re-hospitalization among inpatients with bipolar mania. Further studies are needed in other real-world settings to validate our results.

Alcohol consumption and mortality in patients undergoing coronary artery bypass graft (CABG)-a register-based cohort study

BACKGROUND

Previous studies have shown that compared with abstinence and heavy drinking, moderate alcohol consumption is associated with a reduced risk of mortality among the general population and patients with heart failure and myocardial infarction. We examined the association between alcohol consumption and mortality in coronary artery bypass graft (CABG) patients.

METHOD

We studied 1,919 first-time CABG patients using data on alcohol consumption and mortality obtained from Danish national registers from March 2006 to October 2011. Alcohol consumption was divided into the following groups: abstainers (0 units/week), moderate consumers (1-14 units/week), moderate-heavy drinkers (15-21 units/week) and heavy drinkers (>21 units/week). Hazard ratios (HR) of all-cause mortality were calculated using Cox proportional hazard regression analysis.

RESULTS

The median follow-up was 2.2 years [IQR 2.0]. There were 112 deaths, of which 96 (86 %) were classified as cardiovascular. Adjustments for age and sex showed no increased risk of all-cause mortality for the abstainers (HR 1.61, 95 % CI, 1.00-2.58) and moderate-heavy drinkers (HR 1.40, 95 % CI, 0.73-2.67) compared with moderate consumers. However, heavy drinkers had a high risk of all-cause mortality compared with moderate consumers (HR 2.44, 95 % CI, 1.47-4.04). A full adjustment showed no increase in mortality for the abstainers (HR 1.59, 95 % CI, 0.98-2.57) and moderate-heavy drinkers (HR 1.68, 95 % CI, 0.86-3.29), while heavy drinkers were associated with an increased mortality rate (HR 1.88, 95 % CI, 1.10-3.21). There was no increased risk of 30-day mortality for the abstainers (HR 0.74, 95 % CI, 0.23-2.32), moderate-heavy drinkers (HR 0.36, 95 % CI, 0.07-1.93) and heavy drinkers (HR 2.20, 95 % CI, 0.65-7.36).

CONCLUSION

There was no increased risk of mortality for abstainers (0 units/week) or moderate-heavy drinkers (15-21 units/week) following a CABG. Only heavy drinking (>21 units/week) were significantly associated with an increased mortality rate. These results suggest that only heavy drinking present a risk factor among CABG patients.

Next-generation prognostic assessment for diffuse large B-cell lymphoma

Current standard of care therapy for diffuse large B-cell lymphoma (DLBCL) cures a majority of patients with additional benefit in salvage therapy and autologous stem cell transplant for patients who relapse. The next generation of prognostic models for DLBCL aims to more accurately stratify patients for novel therapies and risk-adapted treatment strategies. This review discusses the significance of host genetic and tumor genomic alterations seen in DLBCL, clinical and epidemiologic factors, and how each can be integrated into risk stratification algorithms. In the future, treatment prediction and prognostic model development and subsequent validation will require data from a large number of DLBCL patients to establish sufficient statistical power to correctly predict outcome. Novel modeling approaches can augment these efforts.