Postdischarge rehospitalization and in-hospital mortality among Taiwanese women with hip fracture

A short length of hospital stay is not associated with risk of readmission among hip fracture patients - a Swedish national register-based cohort study

BACKGROUND

Hospital length of stay (LoS) after a hip fracture likely mirrors health status; however, a too short hospitalization might increase the risk of readmission. In this national register-based study, we investigated the association between LoS after a hip fracture and the risk of readmissions.

METHODS

73,551 patients with a first hip fracture between 2012 and 2019 were followed for 4 months after discharge. LoS was categorized by cubic splines and the association with readmissions was analyzed with Cox regression models.

RESULTS

The mean LoS was 11 ± 6 days and 25% of the study population had at least one readmission. Compared to the mean LoS of 9-12 days, there was a 18% decreased risk of readmission for LoS of 2-4 days (HR 0.82 [95% CI 0.77-0.87]) and 13% decrease for 5-8 days (HR 0.87 [95% CI 0.83-0.91]), when adjusting for sex, age, walking ability, ASA score, CCI, complications during hospitalization and living arrangements. For longer LoS, risk of readmission increased (13-23 days: HR 1.09 [95% CI 1.05-1.13] and 24 + days: HR 1.19 [95% CI 1.11-1.28]). The results were robust across sex, age, and living arrangements. The most common specific reasons for readmission were trauma/injury, cardiovascular and complications, and the proportions did not differ considerably between short and long LoS-categories.

CONCLUSIONS

While a long LoS can be explained by the care need of the patient, a short LoS - compared to the average stay - does not increase the risk of readmission regardless of health status and hospital complications in a Swedish setting.

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

Background

Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture.

Methods

This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC).

Results

For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68.

Conclusions

Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com. External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool.

Trial registration

Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03152-x.

Hospital Length of Stay Reduction Over Time and Patient Readmission for Severe Adverse Events Following Surgery

OBJECTIVE

The aim of the study was to investigate whether patients who undergo surgery in hospitals experiencing significant length of stay (LOS) reductions over time are exposed to a higher risk of severe adverse events in the postoperative period.

SUMMARY BACKGROUND DATA

Surgical care innovation has encouraged hospitals to shorten LOS under financial pressures with uncertain impact on patient outcomes.

METHODS

We selected all patients who underwent elective colectomy or urgent hip fracture repair in French hospitals between 2013 and 2016. For each procedure, hospitals were categorized into 3 groups according to variations in their median LOS as follows: major decrease, moderate decrease, and no decrease. These groups were matched using propensity scores based on patients' and hospitals' potential confounders. Potentially avoidable readmission for severe adverse events and death at 6 months were compared between groups using Cox regressions.

RESULTS

We considered 98,713 patients in 540 hospitals for colectomy and 206,812 patients in 414 hospitals for hip fracture repair before matching. After colectomy, patient outcomes were not negatively impacted when hospitals reduced their LOS [hazard ratio (95% confidence interval): 0.93 (0.78-1.10)]. After hip fracture repair, patients in hospitals with major decreases in LOS had a higher risk of severe adverse events [1.22 (1.11-1.34)] and death [1.17 (1.04-1.32)].

CONCLUSIONS

Patients who underwent surgical procedures in hospitals experiencing major decreases in LOS were demonstrated worse postoperative outcomes after urgent hip fracture repair and not after elective colectomy. Development of care bundles to enhance recovery after emergency surgeries may allow better control of LOS reduction and patient outcomes.

A predictive model for increased hospital length of stay following geriatric hip fracture

BACKGROUND

The purpose of this study was to identify the risk factors that are significantly associated with hospital length of stay (LOS) following geriatric hip fracture and to use these significant variables to develop a LOS calculator.

MATERIALS AND METHODS

This was a retrospective study examining 614 patients treated for geriatric hip fracture between January 2000 and December 2009 at an urban, Level 1 trauma center. A negative binomial regression analysis was used to identify perioperative variables associated with hospital LOS.

RESULTS

614 patients met the inclusion criteria, presenting with a mean age of 78 (±10) years. The most common pre-operative comorbidity was hypertension, followed by diabetes and COPD. After controlling for all collected comorbidities as well as demographics and operative variables, hypertension (IRR: 1.10, p = 0.029) and disseminated cancer (IRR: 1.24, p = 0.007) were found to be significantly associated with LOS. In addition, two demographic/presenting variables, admission to the medicine service (IRR: 1.48, p < 0.001) and male sex (IRR: 1.09, p = 0.034), were shown to be independent risk factors for prolonged LOS. These variables were synthesized into a LOS formula, which estimated LOS to within 3 days of the true length of stay for 0.758 of the series (95% confidence interval: 0.661 to 0.855).

CONCLUSIONS

This study identified several comorbidity and perioperative variables that were significantly associated with LOS following geriatric hip fracture surgery. The resulting LOS model may have utility in the risk stratification of orthopaedic trauma patients presenting with hip fracture.

Predictive risk score for unplanned 30-day rehospitalizations in the French universal health care system based on a medico-administrative database

Background

Reducing unplanned rehospitalizations is one of the priorities of health care policies in France and other Western countries. An easy-to-use algorithm for identifying patients at higher risk of rehospitalizations would help clinicians prioritize actions and care concerning discharge transitions. Our objective was to develop a predictive unplanned 30-day all-cause rehospitalization risk score based on the French hospital medico-administrative database.

Methods

This was a retrospective cohort study of all 2015 discharges from acute-care inpatient hospitalizations in a tertiary-care university center comprising four hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization via emergency departments, and we collected sociodemographic, clinical, and hospital characteristics based on hospitalization database computed for reimbursement of fees. We derived a predictive rehospitalization risk score using a split-sample design and multivariate logistic regression, and we compared the discriminative properties with the LACE index risk-score.

Result

Our analysis included 118,650 hospitalizations, of which 4,127 (3.5%) led to rehospitalizations via emergency departments. Variables independently associated with rehospitalization were age, gender, state-funded medical assistance, as well as disease category and severity, Charlson comorbidity index, hospitalization via emergency departments, length of stay (LOS), and previous hospitalizations 6 months before. The predictive rehospitalization risk score yielded satisfactory discriminant properties (C statistic: 0.74) exceeding the LACE index (0.66).

Conclusion

Our findings indicate that the possibility of unplanned rehospitalization remains high for some patient characteristics, indicating that targeted interventions could be beneficial for patients at the greatest risk. We developed an easy-to-use predictive rehospitalization risk-score of unplanned 30-day all-cause rehospitalizations with satisfactory discriminant properties. Future works should, however, explore if other data from electronic medical records and other databases could improve the accuracy of our predictive rehospitalization risk score based on medico-administrative data.

Nutritional markers may identify patients with greater risk of re-admission after geriatric hip fractures

PURPOSE

Osteoporotic hip fractures are increasing in prevalence with the growing elderly population. Morbidity and mortality remain high following osteoporotic hip fractures despite advances in medical and surgical treatments. The associated costs and medical burdens are increased with a re-admission following hip fracture treatment. This study sought to identify demographic and clinical values that may be a predictive model for 30-day re-admission risk following operative management of an isolated hip fracture.

METHODS

Between January 1, 2013 and April 30, 2015 all patients admitted to a single academic medical centre for treatment of a hip fracture were reviewed. Candidate variables included standard demographics, common laboratory values, and markers of comorbid conditions and nutrition status. A 30-day, all-cause re-admission model was created utilizing multivariate logistic regression.

RESULTS

A total of 607 patients with hip fractures were identified and met the inclusion criteria; of those patients, 67 were re-admitted within 30 days. Univariate analysis indicates that the re-admission group had more comorbidities (p < 0.001) and lower albumin (p = 0.038) and prealbumin (p < 0.001). The final, reduced model contained 12 variables and incorporated four out of five nutritional makers with an internally, cross-validated C-statistic of 0.811 (95% CI: 0.754, 0.867).

CONCLUSION

Our results indicate that specific nutritional laboratory markers at the index admission may identify patients that have a greater risk of re-admission after hip fracture. This model identifies potentially modifiable risk factors and may allow orthogeriatricians to better educate patients and better treat post-operative nutritional status and care.

Predictors of 30-day hospital readmission after hip fracture: a systematic review

BACKGROUND

Early readmission to hospital after hip fracture is associated with increased mortality and significant costs to the healthcare system. There is growing interest in the use of 30-day readmission rates as a metric of hospital performance. Identifying patients at increased risk of readmission after hip fracture may enable pre-emptive action to mitigate this risk and the development of effective methods of risk-adjustment to allow readmission to be used as a reliable measure of hospital performance.

METHODS

We conducted a systematic review of bibliographic databases and reference lists up to July 2016 to identify primary research papers assessing the effect of patient- and hospital-related risk factors for 30-day readmission to hospital after hip fracture.

RESULTS

495 papers were found through electronic and reference search. 65 full papers were assessed for eligibility. 22 met inclusion criteria and were included in the final review. Medical causes of readmission were significantly more common than surgical causes, with pneumonia consistently being cited as the most common readmission diagnosis. Age, pre-existing pulmonary disease and neurological disorders were strong independent predictors of readmission. ASA grade and functional status were more robust predictors of readmission than the Charlson score or individual co-morbidities. Hospital-related risk factors including initial length of stay, hospital size and volume, time to surgery and type of anaesthesia did not have a consistent effect on readmission risk. Discharge location and the strength of hospital-discharge facility linkage were important determinants of risk.

CONCLUSIONS

Patient-related risk factors such as age, co-morbidities and functional status are stronger predictors of 30-day readmission risk after hip fracture than hospital-related factors. Rates of 30-day readmission may not be a valid reflection of hospital performance unless a clear distinction can be made between modifiable and non-modifiable risk factors. We identify a number of deficiencies in the existing literature and highlight key areas for future research.

Risk factors for hospital re-presentation among older adults following fragility fractures: a systematic review and meta-analysis

BACKGROUND

Older adults hospitalized with fragility fractures are at high risk of negative events that can culminate in re-presentations to hospital emergency departments or readmissions to hospital. This systematic review aimed to identify patient, clinical, or hospital-related factors that are identifiable at the index admission and that may be associated with re-presentations to hospital emergency departments or hospital readmissions in older adults following fragility fractures.

METHODS

Four electronic databases (PubMed, CINAHL, Embase, and Scopus) were searched. A suite of search terms identified peer-reviewed English-language articles that examined potential correlates of hospital re-presentation in older adults (mean age ≥ 65 years) who were discharged from hospital following treatment for fragility fractures. A three-stage screening process (titles, abstracts, full text) was conducted by two researchers independently. Participant characteristics, study design, potential correlates examined, analyses, and findings were extracted for studies included in the review. Quality and risk of bias were assessed with the Effective Public Health Practice Project Quality Assessment Tool. The strength of evidence was incorporated into a best evidence synthesis, and meta-analysis was conducted where effect pooling was possible.

RESULTS

Eleven of 35 eligible studies were categorized as high quality studies. These studies reported that age, higher Cumulative Illness Rating scores, American Society of Anesthesiologists scores > 3, longer length of stay, male sex, cardiovascular disease, low post-operative hemoglobin, kidney disease, dementia and cancer were factors identified at the index admission that were predictive of subsequent re-presentation to hospital. Age was the only predictor for which pooling of effects across studies was possible: pooling was conducted for re-presentation ≤ 30 days (pooled OR, 1.27; 95 % CI, 1.14-1.43) and > 30 days (pooled OR, 1.23; 95 % CI, 1.01-1.50).

CONCLUSIONS

The best-evidence synthesis, in addition to the meta-analysis, identified a range of factors that may have utility in guiding clinical practice and policy guidelines for targeted interventions to reduce the need for re-presentation to hospital among this frail clinical population. The paucity of studies investigating re-presentations to hospital emergency departments without admission was an important gap in the literature identified in this review. Key limitations were exclusion of non-English language studies and grey literature.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42015019379 .

An increased risk of epithelial ovarian cancer in Taiwanese women with a new surgico-pathological diagnosis of endometriosis

Background

Epidemiological evidence of relationships between endometriosis and epithelial ovarian cancer (EOC) has been obtained mainly from Western countries. Our goal was to determine the risk of EOC due to endometriosis in Taiwanese women.

Methods

A retrospective cohort study was performed by linking to the National Health Insurance Research Database (NHIRD) of Taiwan. A total of 5,945 women with a new surgico-pathological diagnosis of endometriosis from 2000 to 2010 and 23,780 multivariable-matched controls (1:4) were selected. The Cox regression model adjusted for potential confounders was used to assess the risk of EOC due to endometriosis.

Results

The EOC incidence rate (IR) of the women with and without endometriosis was 11.64 and 2.66 per 10,000 person-years, contributing to a crude hazard ratio (HR) of 4.48 (95% confidence interval [CI] 2.84-7.06), and HR after adjustment for all confounders (adjusted HR) of 5.62 (95% CI 3.46-9.14); the risk was higher in clear-cell carcinoma subtypes (adjusted HR 7.36, 95% CI 1.91-28.33). The EOC IR of women with endometriosis consistently increased with increasing age, ranging from 4.99 (<30 years) to 35.81 (≥50 years) per 10,000 person-years, contributing to a progressively increased risk of EOC (crude HRs ranging from 2.80 to 6.74 and adjusted HRs ranging from 3.34 to 9.63) compared to age-matched women without endometriosis, whose EOC IR also increased with age. The older women (≥50 years) with endometriosis had a risk of EOC that was higher than both the age-matched women without endometriosis (adjusted HR 9.63, 95% CI 3.27-28.37) and the youngest women (<30 years) with endometriosis (adjusted HR 4.97, 95% CI 1.03-24.09).

Conclusions

These significant findings corroborate the previously reported association between endometriosis and increased risk of EOC. Since the risk of EOC in women with a new surgico-pathological diagnosis of endometriosis constantly increased with age and this increased risk of EOC was more significant in women aged ≥50 years, active and intensive surgical intervention should be taken into consideration for older women with endometriosis.