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30-day hospital readmission following otolaryngology surgery: Analysis of a state inpatient database.

ABSTRACT: Determine patient and hospital-level risk factors associated with 30-day readmission for patients undergoing inpatient otolaryngologic surgery.Retrospective cohort study.We analyzed the State Inpatient Database (SID) from California for patients who underwent otolaryngologic surgery between 2008 and 2010. Readmission rates, readmission diagnoses, and patient- and hospital-level risk factors for 30-day readmission were determined. Hierarchical logistic regression modeling was performed to identify procedure-, patient-, and hospital-level risk factors for 30-day readmission.The 30-day readmission rate following an inpatient otolaryngology procedure was 8.1%. The most common readmission diagnoses were nutrition, metabolic, or electrolyte problems (44% of readmissions) and surgical complications (10% of readmissions). New complications after discharge were the major drivers of readmission. Variables associated with 30-day readmission in hierarchical logistic regression modeling were: type of otolaryngologic procedure, Medicare or Medicaid health insurance, chronic anemia, chronic lung disease, chronic renal failure, index admission via the emergency department, in-hospital complication during the index admission, and discharge destination other than home.Approximately one out of 12 patients undergoing otolaryngologic surgery had a 30-day readmission. Readmissions occur across a variety of types of procedures and hospitals. Most of the variability was driven by patient-specific factors, not structural hospital characteristics.4. Laryngoscope, 2016 127:337-345, 2017.

SUBMITTER: Graboyes EM

PROVIDER: S-EPMC5490655 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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30-day hospital readmission following otolaryngology surgery: Analysis of a state inpatient database.

Graboyes Evan M EM Kallogjeri Dorina D Saeed Mohammed J MJ Olsen Margaret A MA Nussenbaum Brian B

The Laryngoscope 20160421 2

<h4>Objectives/hypothesis</h4>Determine patient and hospital-level risk factors associated with 30-day readmission for patients undergoing inpatient otolaryngologic surgery.<h4>Study design</h4>Retrospective cohort study.<h4>Methods</h4>We analyzed the State Inpatient Database (SID) from California for patients who underwent otolaryngologic surgery between 2008 and 2010. Readmission rates, readmission diagnoses, and patient- and hospital-level risk factors for 30-day readmission were determined. ...[more]

PMID: 27098654

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Project description:BackgroundLowering hospital readmission rates has become a primary target for the Centers for Medicare & Medicaid Services, but studies of the relationship between adherence to the recommended hospital care processes and readmission rates have provided inconsistent and inconclusive results.ObjectiveTo examine the association between hospital performance on Medicare's Hospital Compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.Design, setting and participantsWe assessed hospital performance on process measures using the 2007 Hospital Inpatient Quality Reporting Program. The process measures for each condition were aggregated in two separate measures: Overall Measure (OM) and Appropriate Care Measure (ACM) scores. Readmission rates were calculated using Medicare claims.Main outcome measureRisk-standardized 30-day all-cause readmission rate was calculated as the ratio of predicted to expected rate standardized by the overall mean readmission rate. We calculated predicted readmission rate using hierarchical generalized linear models and adjusting for patient-level factors.ResultsAmong patients aged ≥ 66 years, the median OM score ranged from 79.4 % for abdominal surgery to 95.7 % for AMI, and the median ACM scores ranged from 45.8 % for abdominal surgery to 87.9 % for AMI. We observed a statistically significant, but weak, correlation between performance scores and readmission rates for pneumonia (correlation coefficient R = 0.07), AMI (R = 0.10), and orthopedic surgery (R = 0.06). The difference in the mean readmission rate between hospitals in the 1st and 4th quartiles of process measure performance was statistically significant only for AMI (0.25 percentage points) and pneumonia (0.31 percentage points). Performance on process measures explained less than 1 % of hospital-level variation in readmission rates.ConclusionsHospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Project description:BackgroundThe goal of this study was to investigate factors associated with 30-day readmission in a multivariate model, including the CDC wound classes "clean," "clean/contaminated," "contaminated," and "dirty/infected."MethodsThe 2017-2020 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all patients undergoing total hip replacement, coronary artery bypass grafting, Ivor Lewis esophagectomy, pancreaticoduodenectomy, distal pancreatectomy, pneumonectomy, and colectomies. ACS-defined wound classes were concordant with CDC definitions. Multivariate linear mixed regression was used to determine risk factors for readmission while adjusting for type of surgery as a random intercept.Results477,964 cases were identified, with 38,734 (8.1%) patients having experienced readmission within 30 days of surgery. There were 181,243 (37.9%) cases classified as wound class "clean", 215,729 (45.1%) cases classified as "clean/contaminated", 40,684 cases (8.5%) classified as "contaminated", and 40,308 (8.4%) cases classified as "dirty/infected". In the multivariate generalized mixed linear model adjusting for type of surgery, sex, body mass index, race, American Society of Anesthesiologists class, presence of comorbidity, length of stay, urgency of surgery, and discharge destination, "clean/contaminated" (p < .001), "contaminated" (p < .001), and "dirty/infected" (p < .001) wound classes (when compared to "clean") were significantly associated with 30-day readmission. Organ/space surgical site infection and sepsis were among the most common reasons for readmission in all wound classes.ConclusionsWound classification was strongly prognostic for readmission in multivariable models, suggesting that it may serve as a marker of readmissions. Surgical procedures that are "non-clean" are at significantly greater risk for 30-day readmission. Readmissions may be due to infectious complications; optimizing antibiotic use or source control to prevent readmission are areas of future study.

Project description:PURPOSE:Analyze associations between patients' characteristics and treatment factors with 30-day unplanned readmissions in hospitalized otolaryngology patients in the German Diagnosis Related Group (D-DRG) system. METHODS:A retrospective cohort study was performed on 15.271 otolaryngology admissions of 12.859 patients in 2015 in Thuringia, Germany. The medical records of the 1173 cases (7.6%) with readmission within 30-days were analyzed in detail. RESULTS:The 30-day readmission was planned in 747 cases (4.9%) and was unplanned in 422 cases (2.8%). The median interval between primary and next inpatient treatment was 11 days. The principal diagnosis was the same as during the primary index treatment in 72% of the cases. The most frequent reasons for readmission were: Need for non-surgical therapy (31.2%), need for further surgery (26.3%), post-surgical complaints (16.9%), and recurrence of primary complaints (10.7%). The multivariate analysis revealed that discharge due to patient's request against medical advice was a strong independent factor with high risk for unplanned readmission (Odds Ratio [OR] = 9.62]; confidence interval [CI] = 2.69-34.48). Surgery at index admission (OR = 3.33; CI = 1.86-5.96) was the second important independent risk factor for unplanned readmission. Unplanned readmission had more frequently a non-surgical treatment at readmission than a surgical treatment (OR = 3.92; CI = 2.24-6.84) and needed more frequently further diagnostics (OR = 2.34; CI = 1.34-4.11). The following index International Classification of Diseases (ICD) categories had the highest risk for unplanned readmission: Injury, poisoning and certain other consequences of external causes, ICD: S00-T98 (OR = 66.67; CI = 15.87-333.33), symptoms, signs, abnormal findings, ill-defined causes, not otherwise classified, ICD: R00-R99 (OR = 62.5; CI = 11.76-333.33), blood forming organ diseases, ICD: D50-D90 (OR = 21.276; CI = 3.508-125), and eye/ ear diseases, ICD: H00-H95 (OR = 12.66; CI = 4.29-37.03). CONCLUSIONS:The causes of unplanned 30-day readmission in German otolaryngology inpatients are multifactorial. Specific patient and treatment characteristics were identified to be targeted with health care interventions to decrease unplanned readmissions.

Project description:BackgroundHospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.MethodsThis population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance.ResultsOf the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance.ConclusionThe performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Project description:ObjectivesReadmission to acute care from the inpatient rehabilitation facility (IRF) setting is potentially preventable and an important target of quality improvement and cost savings. The objective of this study was to develop a risk calculator to predict 30-day all-cause readmissions from the IRF setting.DesignRetrospective database analysis using the Uniform Data System for Medical Rehabilitation (UDSMR) from 2015 through 2019.Setting and participantsIn total, 956 US inpatient rehabilitation facilities and 1,849,768 IRF discharges comprising patients from 14 impairment groups.MethodsLogistic regression models were developed to calculate risk-standardized 30-day all-cause hospital readmission rates for patients admitted to an IRF. Models for each impairment group were assessed using 12 common clinical and demographic variables and all but 4 models included various special variables. Models were assessed for discrimination (c-statistics), calibration (calibration plots), and internal validation (bootstrapping). A readmission risk scoring system was created for each impairment group population and was graphically validated.ResultsThe mean age of the cohort was 68.7 (15.2) years, 50.7% were women, and 78.3% were Caucasian. Medicare was the primary payer for 73.1% of the study population. The final models for each impairment group included between 4 and 13 total predictor variables. Model c-statistics ranged from 0.65 to 0.70. There was good calibration represented for most models up to a readmission risk of 30%. Internal validation of the models using bootstrap samples revealed little bias. Point systems for determining risk of 30-day readmission were developed for each impairment group.Conclusions and implicationsMultivariable risk factor algorithms based upon administrative data were developed to assess 30-day readmission risk for patients admitted from IRF. This report represents the development of a readmission risk calculator for the IRF setting, which could be instrumental in identifying high risk populations for readmission and targeting resources towards a diverse group of IRF impairment groups.

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30-day hospital readmission following otolaryngology surgery: Analysis of a state inpatient database.

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30-day hospital readmission following otolaryngology surgery: Analysis of a state inpatient database.

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