Dataset Information

Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study.

ABSTRACT:

Background

Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models.

Objective

This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task.

Methods

We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded.

Results

The proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability.

Conclusions

Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.

SUBMITTER: Mohammadi R

PROVIDER: S-EPMC7732713 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study.

Mohammadi Ramin R Jain Sarthak S Namin Amir T AT Scholem Heller Melissa M Palacholla Ramya R Kamarthi Sagar S Wallace Byron B

JMIR medical informatics 20201127 11

<h4>Background</h4>Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain ...[more]

PMID: 33245283

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Project description:IntroductionThe objective of this study was to assess the number and magnitude of preoperative expectations and to correlate them with the degree of satisfaction expressed one year after Total Hip Arthroplasty (THA) or Total Knee Arthroplasty (TKA), in patients with severe and painful osteoarthritis (OA).Materials and methodsPreoperative expectations (within 20 days prior to surgery) and postoperative satisfaction (one year after the intervention) were measured using the previously validated French version of the Hospital for Special Surgery Hip or Knee Replacement Expectations Survey. Postoperative satisfaction was measured using a specific scale, following the same methodology as that used for the assessment of expectations. Prediction of the satisfaction of the patients was performed using multivariate linear regression modelling.ResultsA total of 138 patients (80 THA and 58 TKA) completed the two parts of the study. The expectations score (mean ± SD) (range 0-100) was 72.58 ± 12.63 before THA and 69.10 ± 13.72 before TKA (p = 0.13). The number of expectations expressed was 14.34 ± 1.32 (out of a potential maximum of 18) before THA and 14.70 ± 2.29 (out of a potential maximum of 19) before TKA. After 1 year, THA generated a significantly higher degree of satisfaction compared to TKA (69.70 ± 14.46 v 60.44 ± 17.54, p<0.001) (range 0-100). The pre-operative expectations score was the single best positive predictor of the post-surgery satisfaction assessment both for TKA and THA.ConclusionPatients undergoing total joint arthroplasty for end-stage OA have a high level of expectations, before both THA and TKA. While both types of interventions significantly improve essential and non-essential activities, the rate of satisfaction is significantly greater post THA. Preoperative expectations are a major contributor to the final degree of satisfaction, one year after surgery. These results re-emphasize the need for an optimal preoperative interaction between health care providers and patients, to allow patients a chance to foresee a reasonable outcome after TJA.

Project description:ImportanceThe Hospital Readmissions Reduction Program (HRRP) is a Centers for Medicare and Medicaid Services policy that levies hospital reimbursement penalties based on excess readmissions of patients with 4 medical conditions and 3 surgical procedures. A greater understanding of factors associated with the 3 surgical reimbursement penalties is needed for clinicians in surgical practice.ObjectiveTo investigate the first year of HRRP readmission penalties applied to 2 surgical procedures-elective total hip arthroplasty (THA) and total knee arthroplasty (TKA)-in the context of hospital and patient characteristics.Design, setting, and participantsFiscal year 2015 HRRP penalization data from Hospital Compare were linked with the American Hospital Association Annual Survey and with the Healthcare Cost and Utilization Project State Inpatient Database for hospitals in the state of Florida. By using a case-control framework, those hospitals were separated based on HRRP penalty severity, as measured with the HRRP THA and TKA excess readmission ratio, and compared according to orthopedic volume as well as hospital-level and patient-level characteristics. The first year of HRRP readmission penalties applied to surgery in Florida Medicare subsection (d) hospitals was examined, identifying 60 663 Medicare patients who underwent elective THA or TKA in 143 Florida hospitals. The data analysis was conducted from February 2016 to January 2017.ExposuresAnnual hospital THA and TKA volume, other hospital-level characteristics, and patient factors used in HRRP risk adjustment.Main outcomes and measuresThe HRRP penalties with HRRP excess readmission ratios were measured, and their association with annual THA and TKA volume, a common measure of surgical quality, was evaluated. The HRRP penalties for surgical care according to hospital and readmitted patient characteristics were then examined.ResultsAmong 143 Florida hospitals, 2991 of 60 663 Medicare patients (4.9%) who underwent THA or TKA were readmitted within 30 days. Annual hospital arthroplasty volume seemed to follow an inverse association with both unadjusted readmission rates (r = -0.16, P = .06) and HRRP risk-adjusted readmission penalties (r = -0.12, P = .14), but these associations were not statistically significant. Other hospital characteristics and readmitted patient characteristics were similar across HRRP orthopedic penalty severity.Conclusions and relevanceThis study's findings suggest that higher-volume hospitals had less severe, but not significantly different, rates of readmission and HRRP penalties, without systematic differences across readmitted patients.

Dataset Information

Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study.

Background

Objective

Methods

Results

Conclusions

Publications

Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study.

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