Postoperative 30-day Readmission

Morris, Melanie S.; Graham, Laura A.; Richman, Joshua S.; Hollis, Robert H.; Jones, Caroline E.; Wahl, Tyler S.; Itani, Kamal M.F.; Mull, Hillary J.; Rosen, Amy K.; Copeland, Laurel A.; Burns, Edith; Telford, Gordon L.; Whittle, Jeff; Wilson, Mark A.; Knight, Sara J.; Hawn, Mary T.

doi:10.1097/sla.0000000000001855

Cited by 57 publications

(51 citation statements)

References 15 publications

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“…All validations were done internally; most were conducted through retrospective validation (n=37) and used split sample (n=24) or cross validation (n=11) methods. The C statistics ranged between 0.52 and 0.90,2324 with 17 studies reporting a C statistic of 0.75 or greater 1112141516171923293334363742434647…”

Section: Resultsmentioning

confidence: 99%

“…Using EMR data, detailed clinical and healthcare encounter data such as admission type and discharge location, primary and additional diagnoses, morbidities, laboratory results, vital signs, type and number of drugs, and basic demographics such as age, sex, race and ethnicity, and insurance type were readily available and thus examined in most of the predictive models. Additionally, Escobar et al and Morris et al used length of operating room stay in hours as a proxy for complexity of surgical procedure if the inpatient hospital stay included any surgical procedure 1217. Being admitted to an intensive care unit and number of procedures during the index hospital stay have also been used as proxies for the complexity of a patient’s condition 15181922252728303843…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Mahmoudi

Kamdar

Kim

et al. 2020

BMJ

127

128

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Objective To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019. Eligibility criteria for selecting studies All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data. Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models. Results Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Mahmoudi

Kamdar

Kim

et al. 2020

BMJ

127

128

View full text Add to dashboard Cite

show abstract

“…For example, a review of surgical readmissions by Merkow and colleagues 17 found the most common reasons for a readmission included a surgical site infection, obstruction or bleeding. Similarly, Morris and colleagues 18 also noted that a large proportion of surgical readmissions were to manage complications that in some cases may not be avoidable. In such situations, a readmission should be seen as valuable rather than a sign of poor quality care.…”

Section: Discussionmentioning

confidence: 96%

“…First, by using administrative claims data we may not fully capture the circumstances around a readmission to appropriately risk-adjust rates for hospitals. Several important readmissions studies have identified disparities 15,19 , systems 20 and socioeconomic factors 18,21 not currently included in our risk-adjustment models. Although the Centers for Medicare and Medicaid Services is considering modification to their risk-adjustment methodology 22,23 , we chose to use the same risk-adjustment framework currently in place to make out findings consistent with other studies assessing also examining Hospital Readmissions Reduction Program in Medicare claims data 3,13–15,19,24 .…”

Section: Discussionmentioning

confidence: 99%

Impact of the Hospital Readmission Reduction Program on Surgical Readmissions Among Medicare Beneficiaries

et al. 2017

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Objective To understand the impact of the Hospital Readmission Reduction Program on both future targeted and non-targeted surgical procedures. Summary Background Data The Hospital Readmission Reduction Program, established under the Affordable Care Act in March of 2010, placed financial penalties on hospitals with higher-than-expected rates of readmission beginning in 2012 for targeted medical conditions. Multiple studies have suggested a “spill-over” effect into other conditions, but the extent of that effect for specific surgical procedures is unknown. Methods A retrospective review 5,122,240 Medicare beneficiaries who underwent future targeted procedures (total hip replacement, total knee replacements) or non-targeted procedures (colectomy, lung resection, abdominal aortic aneurysm repair, coronary artery bypass graft, aortic valve replacement, mitral valve repair) using an interrupted time-series model to assess the rates of readmission before the Hospital Readmission Reduction Program was announced (2008 to 2010), while the program was being implemented (2010 to 2012) and after penalties were initiated (2012 to 2014). We also explored if the change in readmission rates were correlated with changes in index length of stay, use of observation status or discharge to a skilled nursing facility. Results From 2008 to 2014 rates of readmission declined for both target conditions (6.8% to 4.8%; slope change −0.07 to −0.10, p<0.001) and non-target conditions (17.1% to 13.4%; slope change −0.04 to −0.11, p<0.001). The rate of reduction was most prominent after announcement of the program between 2010 and 2012 for both targeted and non-targeted conditions. During the same time period, mean hospital length of stay decreased; non-targeted conditions (10.4 days to 8.4 days) and targeted conditions (3.6 days to 2.8 days). There was no correlation between hospital reduction in readmissions and use of observation-only admissions (Pearson Correlation Coefficient=0.01) or discharge to a skilled nursing facility (Pearson Correlation Coefficient=0.05). Conclusions Trends in readmissions after inpatient surgery are consistent with hospitals responding to financial incentives announced in the Hospital Readmission Reduction Program. There appears to be both an anticipatory effect (future targeted procedures reducing readmission before payments implemented) as well as a spillover effect (non-targeted procedures also reducing readmissions).

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“…Some of the large data analyses have been completed, quantifying preoperative, intraoperative and postoperative factors as important but limited predictors of readmission (10% of variance explained) [38]. Collection of psychosocial data from the prospective cohort is underway, as is the process of understanding reasons for readmission per the panel of experts.…”

Section: Discussionmentioning

confidence: 99%

A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology

Copeland

Graham

Richman

et al. 2017

BMC Health Serv Res

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BackgroundHospital readmissions are associated with higher resource utilization and worse patient outcomes. Causes of unplanned readmission to the hospital are multiple with some being better targets for intervention than others. To understand risk factors for surgical readmission and their incremental contribution to current Veterans Health Administration (VA) surgical quality assessment, the study, Improving Surgical Quality: Readmission (ISQ-R), is being conducted to develop a readmission risk prediction tool, explore predisposing and enabling factors, and identify and rank reasons for readmission in terms of salience and mutability.MethodsHarnessing the rich VA enterprise data, predictive readmission models are being developed in data from patients who underwent surgical procedures within the VA 2007–2012. Prospective assessment of psychosocial determinants of readmission including patient self-efficacy, cognitive, affective and caregiver status are being obtained from a cohort having colorectal, thoracic or vascular procedures at four VA hospitals in 2015–2017. Using these two data sources, ISQ-R will develop readmission categories and validate the readmission risk prediction model. A modified Delphi process will convene surgeons, non-surgeon clinicians and quality improvement nurses to rank proposed readmission categories vis-à-vis potential preventability.DiscussionISQ-R will identify promising avenues for interventions to facilitate improvements in surgical quality, informing specifications for surgical workflow managers seeking to improve care and reduce cost. ISQ-R will work with Veterans Affairs Surgical Quality Improvement Program (VASQIP) to recommend potential new elements VASQIP might collect to monitor surgical complications and readmissions which might be preventable and ultimately improve surgical care.

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Postoperative 30-day Readmission

Abstract: Postoperative readmissions are difficult to predict at the time of discharge, and of information available at that time, preoperative factors are the most important.

Cited by 57 publications

References 15 publications

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Impact of the Hospital Readmission Reduction Program on Surgical Readmissions Among Medicare Beneficiaries

A study to reduce readmissions after surgery in the Veterans Health Administration: design and methodology

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