Hospital Readmissions: Are They Preventable?

Incidence and risk factors for unplanned readmission after colorectal surgery: A meta-analysis

BACKGROUND

Unplanned readmissions (URs) after colorectal surgery (CRS) are common, expensive, and result from failure to progress in postoperative recovery. These are considered preventable, although the true extent is yet to be defined. In addition, their successful prediction remains elusive due to significant heterogeneity in this field of research. This systematic review and meta-analysis of observational studies aimed to identify the clinically relevant predictors of UR after colorectal surgery.

METHODS

A systematic review was conducted using indexed sources (The Cochrane Database of Systematic Reviews, MEDLINE, and Embase) to search for published studies in English between 1996 and 2022. The search strategy returned 625 studies for screening of which, 150 were duplicates, and 305 were excluded for irrelevance. An additional 150 studies were excluded based on methodology and definition criteria. Twenty studies met the inclusion criteria and for the meta-analysis. Independent meta-extraction was conducted by multiple reviewers (JD & SR) in accordance with PRISMA guidelines. The primary outcome was defined as UR within 30 days of index discharge after colorectal surgery. Data were pooled using a random-effects model. Risk of bias was assessed using the Quality in Prognosis Studies tool.

RESULTS

The reported 30-day UR rate ranged from 6% to 22.8%. Increased comorbidity was the strongest preoperative risk factor for UR (OR 1.39, 95% CI 1.28-1.51). Stoma formation was the strongest operative risk factor (OR 1.54, 95% CI 1.38-1.72). The occurrence of postoperative complications was the strongest postoperative and overall risk factor for UR (OR 3.03, 95% CI 1.21-7.61).

CONCLUSIONS

Increased comorbidity, stoma formation, and postoperative complications are clinically relevant predictors of UR after CRS. These risk factors are readily identifiable before discharge and serve as clinically relevant targets for readmission risk-reducing strategies. Successful readmission prediction may facilitate the efficient allocation of healthcare resources.

Readmission prediction after colorectal cancer surgery: A derivation and validation study

BACKGROUND

Unplanned readmissions after colorectal cancer (CRC) surgery are common, expensive, and result from failure to progress in postoperative recovery. The context of their preventability and extent of predictability remains undefined. This study aimed to define the 30-day unplanned readmission (UR) rate after CRC surgery, identify risk factors, and develop a prediction model with external validation.

METHODS

Consecutive patients who underwent CRC surgery between 2012 and 2017 at Christchurch Hospital were retrospectively identified. The primary outcome was UR within 30 days after index discharge. Statistically significant risk factors were identified and incorporated into a predictive model. The model was then externally evaluated on a prospectively recruited dataset from 2018 to 2019.

RESULTS

Of the 701 patients identified, 15.1% were readmitted within 30 days of discharge. Stoma formation (OR 2.45, 95% CI 1.59-3.81), any postoperative complications (PoCs) (OR 2.27, 95% CI 1.48-3.52), high-grade PoCs (OR 2.52, 95% CI 1.18-5.11), and rectal cancer (OR 2.11, 95% CI 1.48-3.52) were statistically significant risk factors for UR. A clinical prediction model comprised of rectal cancer and high-grade PoCs predicted UR with an AUC of 0.64 and 0.62 on internal and external validation, respectively.

CONCLUSIONS

URs after CRC surgery are predictable and occur within 2 weeks of discharge. They are driven by PoCs, most of which are of low severity and develop after discharge. Atleast 16% of readmissions are preventable by management in an outpatient setting with appropriate surgical expertise. Targeted outpatient follow-up within two weeks of discharge is therefore the most effective transitional-care strategy for prevention.

The preoperative risk tool SURPAS accurately predicts outcomes in emergency surgery

BACKGROUND

The Surgical Risk Preoperative Assessment System (SURPAS) uses eight variables to accurately predict postoperative complications but has not been sufficiently studied in emergency surgery. We evaluated SURPAS in emergency surgery, comparing it to the Emergency Surgery Score (ESS).

METHODS

SURPAS and ESS estimates of 30-day mortality and overall morbidity were calculated for emergency operations in the 2009-2018 ACS-NSQIP database and compared using observed-to-expected plots and rates, c-indices, and Brier scores. Cases with incomplete data were excluded.

RESULTS

In 205,318 emergency patients, SURPAS underestimated (8.1%; 35.9%) while ESS overestimated (10.1%; 43.8%) observed mortality and morbidity (8.9%; 38.8%). Each showed good calibration on observed-to-expected plots. SURPAS had better c-indices (0.855 vs 0.848 mortality; 0.802 vs 0.755 morbidity), while the Brier score was better for ESS for mortality (0.0666 vs. 0.0684) and for SURPAS for morbidity (0.1772 vs. 0.1950).

CONCLUSIONS

SURPAS accurately predicted mortality and morbidity in emergency surgery using eight predictor variables.

Comparison of administrative data and the American College of Surgeons National Surgical Quality Improvement Program data in a New South Wales Hospital

BACKGROUND

The National Surgical Quality Improvement Program (NSQIP) is widely used in North America for benchmarking. In 2015, NSQIP was introduced to four New South Wales public hospitals. The aim of this study is to investigate the agreement between NSQIP and administrative data in the Australian setting; to compare the performance of models derived from each data set to predict 30-day outcomes.

METHODS

The NSQIP and administrative data variables were mapped to select variables available in both data sets where coding may be influenced by interpretation of the clinical information. These were compared for agreement. Logistic regression models were fitted to estimate the probability of adverse outcomes within 30 days. Models derived from NSQIP and administrative data were compared by receiver operating characteristic curve analysis.

RESULTS

A total of 2240 procedures over 21 months had matching records. Functional status demonstrated poor agreement (kappa 0.02): administrative data recorded only one (1%) patient with partial- or total-dependence as recorded by NSQIP data. The American Society of Anesthesiologists class demonstrated excellent agreement (kappa 0.91). Other perioperative variables demonstrated poor to fair agreement (kappa 0.12-0.61). Predictive model based on NSQIP data was excellent at predicting mortality but was less accurate for complications and readmissions. The NSQIP model was better in predicting mortality and complications (receiver operating characteristic curve 0.93 versus 0.87; P = 0.029 and 0.71 versus 0.64; P = 0.027).

CONCLUSIONS

There is poor agreement between NSQIP data and administrative data. Predictive models associated with NSQIP data were more accurate at predicting surgical outcomes than those from administrative data. To drive quality improvement in surgery, high-quality clinical data are required and we believe that NSQIP fulfils this function.

Unplanned readmission after lung resection surgery: A systematic review

Urgent readmissions have a major impact on outcomes in patient health and healthcare costs. The associated risk factors have generally been infrequently studied. The main objective of the present work is to identify pre- and perioperative determinants of readmission; the secondary aim was to determine readmission rate, identification of readmission diagnoses, and impact of readmissions on survival rates in related analytical studies. The review was performed through a systematic search in the main bibliographic databases. In the end, 19 papers met the selection criteria. The main risk factors were: sociodemographic patient variables; comorbidities; type of resection; postoperative complications; long stay. Despite the great variability in the published studies, all highlight the importance of reducing readmission rates because of the significant impact on patients and the healthcare system.

Transitional care services: a quality and safety process improvement program in neurosurgery

OBJECTIVE

Readmissions increasingly serve as a metric of hospital performance, inviting quality improvement initiatives in both medicine and surgery. However, few readmission reduction programs have targeted surgical patient populations. The objective of this study was to establish a transitional care program (TCP) with the goal of decreasing length of stay (LOS), improving discharge efficiency, and reducing readmissions of neurosurgical patients by optimizing patient education and postdischarge surveillance.

METHODS

Patients undergoing elective cranial or spinal neurosurgery performed by one of 5 participating surgeons at a quaternary care hospital were enrolled into a multifaceted intervention. A preadmission overview and establishment of an anticipated discharge date were both intended to set patient expectations for a shorter hospitalization. At discharge, in-hospital prescription filling was provided to facilitate medication compliance. Extended discharge appointments with a neurosurgery TCP-trained nurse emphasized postoperative activity, medications, incisional care, nutrition, signs that merit return to medical attention, and follow-up appointments. Finally, patients received a surveillance phone call 48 hours after discharge. Eligible patients omitted due to staff limitations were selected as controls. Patients were matched by sex, age, and operation type—key confounding variables—with control patients, who were eligible patients treated at the same time period but not enrolled in the TCP due to staff limitation. Multivariable logistic regression evaluated the association of TCP enrollment with discharge time and readmission, and linear regression with LOS. Covariates included matching criteria and Charlson Comorbidity Index scores.

RESULTS

Between 2013 and 2015, 416 patients were enrolled in the program and matched to a control. The median patient age was 55 years (interquartile range 44.5–65 years); 58.4% were male. The majority of enrolled patients underwent spine surgery (59.4%, compared with 40.6% undergoing cranial surgery). Hospitalizations averaged 62.1 hours for TCP patients versus 79.6 hours for controls (a 16.40% reduction, 95% CI 9.30%–23.49%; p < 0.001). The intervention was associated with a higher proportion of morning discharges, which was intended to free beds for afternoon admissions and improve patient flow (OR 3.13, 95% CI 2.27–4.30; p < 0.001), and decreased 30-day readmissions (2.5% vs 5.8%; OR 2.43, 95% CI 1.14–5.27; p = 0.02).

CONCLUSIONS

This neurosurgical TCP was associated with a significantly shorter LOS, earlier discharge, and reduced 30-day readmission after elective neurosurgery. These results underscore the importance of patient education and surveillance after hospital discharge.

Pediatric surgical readmissions: Are they truly preventable?

BACKGROUND/PURPOSE

Reimbursement penalties for excess hospital readmissions have begun for the pediatric population. Therefore, research determining incidence and predictors is critical.

METHODS

A retrospective review of University HealthSystem Consortium database (N=258 hospitals; 2,723,621 patients) for pediatric patients (age 0-17years) hospitalized from 9/2011 to 3/2015 was performed. Outcome measures were 7-, 14-, and 30-day readmission rates. Hospital and patient characteristics were evaluated to identify predictors of readmission.

RESULTS

Readmission rates at 7, 14, and 30days were 2.1%, 3.1%, and 4.4%. For pediatric surgery patients (N=260,042), neither index hospitalization length of stay (LOS) nor presence of a complication predicted higher readmissions. Appendectomy was the most common procedure leading to readmission. Evaluating institutional data (N=5785), patients admitted for spine surgery, neurosurgery, transplant, or surgical oncology had higher readmission rates. Readmission diagnoses were most commonly infectious (37.2%) or for nausea/vomiting/dehydration (51.1%). Patients with chronic medical conditions comprised 55.8% of patients readmitted within 7days. 92.0% of patients requiring multiple rehospitalizations had comorbidities.

CONCLUSIONS

Readmission rates for pediatric patients are significantly lower than adults. Risk factors for adult readmissions do not predict pediatric readmissions. Readmission may be a misnomer for the pediatric surgical population, as most are related to chronic medical conditions and other nonmodifiable risk factors.

LEVEL OF EVIDENCE

Level IV.