Readmission following surgical stabilization of rib fractures: Analysis of incidence, cost, and risk factors using the Nationwide Readmissions Database

The burden of readmissions after rib fractures among older adults

BACKGROUND

The index hospitalization morbidity and mortality of rib fractures among older adults (aged ≥65 years) is well-known, yet the burden and risks for readmissions after rib fractures in this vulnerable population remain understudied. We aimed to characterize the burdens and etiologies associated with 3-month readmissions among older adults who suffer rib fractures. We hypothesized that readmissions would be common and associated with modifiable etiologies.

METHODS

This survey-weighted retrospective study using the 2017 and 2019 National Readmissions Database evaluated adults aged ≥65 years hospitalized with multiple rib fractures and without major extrathoracic injuries. The main outcome was the proportion of patients experiencing all-cause 3-month readmissions. We assessed the 5 leading principal readmission diagnoses overall and delineated them by index hospitalization discharge disposition (home or facility). Sensitivity analysis using clinical classification categories characterized readmissions that could reasonably represent rib fracture-related sequelae.

RESULTS

In 2017, 25,092 patients met the inclusion criteria, with 20% (N = 4,894) experiencing 3-month readmissions. Six percent of patients did not survive their readmission. The 5 leading principal readmission diagnoses were sepsis (many associated with secondary diagnoses of pneumonia [41%] or urinary tract infections [41%]), hypertensive heart/kidney disease, hemothorax, pneumonia, and respiratory failure. In 2019, a comparable 3-month readmission rate of 23% and identical 5 leading diagnoses were found. Principal readmission diagnosis of hemothorax was associated with the shortest time to readmission (median [interquartile range]:9 [5-23] days). Among patients discharged home after index hospitalization, pleural effusion-possibly representing mischaracterized hemothorax-was among the leading principal readmission diagnoses. Some patients readmitted with a principal diagnosis of hemothorax or pleural effusion had these diagnoses at index hospitalization; a lower proportion of these patients underwent pleural fluid intervention during index hospitalization compared with readmission. On sensitivity analysis, 30% of 3-month readmissions were associated with principal diagnoses suggesting rib fracture-related sequelae.

CONCLUSION

Readmissions are not infrequent among older adults who suffer rib fractures, even in the absence of major extrathoracic injuries. Future studies should better characterize how specific complications associated with readmissions, such as pneumonia, urinary tract infections, and delayed hemothoraces, could be mitigated.

Does the timing of surgery affect outcomes in young and middle-aged patients undergoing surgical stabilization of rib fractures? Feedback was based on real data from physicians, patients, and family caregivers

BACKGROUND

There is a general clinical consensus that early surgical stabilization of rib fractures (SSRF, ≤ 48-72 h after admission) can benefit patients, and this is only regarding the surgeon's opinions. This study assessed the true outcomes of young and middle-aged patients at different surgical timings.

METHODS

This retrospective cohort study was conducted among patients aged 30-55 years who were hospitalized with a diagnosis of isolated rib fractures and underwent SSRF between July 2017 and September 2021. The patients were divided into early (≤ 3 days), mid- (4-7 days) and late (8-14 days) groups, according to the interval (days) between surgery and injury date. The impact of different surgical timings on clinical outcomes, patients, and families was assessed by comparing SSRF-related data during hospitalization and follow-up studies of clinicians, patients themselves, and family caregivers 1-2 months after surgery.

RESULTS

In this study, 155 complete patient data were finally included, including 52, 64, and 39 patients in the early, mid, and late groups, respectively. Length of operation, preoperative closed chest drainage rate, length of hospital stay, intensive care unit length of stay, duration of invasive mechanical ventilation in the early group were lower than those in the intermediate and late groups. Additionally, hemothorax and excess pleural fluid incidence after SSRF was lower in the early group than in the intermediate and late groups. Postoperative follow-up results showed that patients in the early group had higher SF-12 physical component summary scores and shorter duration of absence from work. Family caregivers had lower Zarit Burden Interview scores than those in the mid- and late groups.

CONCLUSION

From the experience of our institution's SSRF, early surgery is safe and offers additional potential benefits for young and middle-aged patients and families with isolated rib fractures.

DeepBackRib: Deep learning to understand factors associated with readmissions after rib fractures

BACKGROUND

Deep neural networks yield high predictive performance, yet obscure interpretability limits clinical applicability. We aimed to build an explainable deep neural network that elucidates factors associated with readmissions after rib fractures among nonelderly adults, termed DeepBackRib . We hypothesized that DeepBackRib could accurately predict readmissions and a game theoretic approach to elucidate how predictions are made would facilitate model explainability.

METHODS

We queried the 2017 National Readmissions Database for index hospitalization encounters of adults aged 18 to 64 years hospitalized with multiple rib fractures. The primary outcome was 3-month readmission(s). Study cohort was split 60-20-20 into training-validation-test sets. Model input features included demographic/injury/index hospitalization characteristics and index hospitalization International Classification of Diseases, Tenth Revision , diagnosis codes. The seven-layer DeepBackRib comprised multipronged strategies to mitigate overfitting and was trained to optimize recall. Shapley additive explanation analysis identified the marginal contribution of each input feature for predicting readmissions.

RESULTS

A total of 20,260 patients met the inclusion criteria, among whom 11% (n = 2,185) experienced 3-month readmissions. Feature selection narrowed 3,164 candidate input features to 61, and DeepBackRib yielded 91%, 85%, and 82% recall on the training, validation, and test sets, respectively. Shapley additive explanation analysis quantified the marginal contribution of each input feature in determining DeepBackRib's predictions: underlying chronic obstructive pulmonary disease and long index hospitalization length of stay had positive associations with 3-month readmissions, while private primary payer and diagnosis of pneumothorax during index admission had negative associations.

CONCLUSION

We developed and internally validated a high-performing deep learning algorithm that elucidates factors associated with readmissions after rib fractures. Despite promising predictive performance, standalone deep learning algorithms are insufficient for clinical prediction tasks: a concerted effort is needed to ensure that clinical prediction algorithms remain explainable.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Data Resources for Evaluating the Economic and Financial Consequences of Surgical Care in the United States

LEVEL OF EVIDENCE

V.