Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm

BACKGROUND: Unplanned hospital readmissions are common, expensive and often preventable. Strategies designed to reduce readmissions should target patients at high risk. The purpose of this study was to describe medical patients identified using a recently published and validated algorithm (the LACE index) as being at high risk for readmission and to examine their actual hospital readmission rates. METHODS: We used population-based administrative data to identify adult medical patients discharged alive from 6 hospitals in Toronto, Canada, during 2007. A LACE index score of 10 or higher was used to identify patients at high risk for readmission. We described patient and hospitalization characteristics among both the high-risk and low-risk groups as well as the 30-day readmission rates. RESULTS: Of 26 045 patients, 12.6% were readmitted to hospital within 30 days and 20.9% were readmitted within 90 days of discharge. High-risk patients (LACE ≥ 10) accounted for 34.0% of the sample but 51.7% of the patients who were readmitted within 30 days. High-risk patients were readmitted with twice the frequency as other patients, had longer lengths of stay and were more likely to die during the readmission. INTERPRETATION: Using a LACE index score of 10, we identified patients with a high rate of readmission who may benefit from improved post-discharge care. Our findings suggest that the LACE index is a potentially useful tool for decision-makers interested in identifying appropriate patients for post-discharge interventions

Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study

<p>Abstract</p> <p>Background</p> <p>Clinicians informally assess changes in patients' status over time to prognosticate their outcomes. The incorporation of trends in patient status into regression models could improve their ability to predict outcomes. In this study, we used a unique approach to measure trends in patient hospital death risk and determined whether the incorporation of these trend measures into a survival model improved the accuracy of its risk predictions.</p> <p>Methods</p> <p>We included all adult inpatient hospitalizations between 1 April 2004 and 31 March 2009 at our institution. We used the daily mortality risk scores from an existing time-dependent survival model to create five trend indicators: absolute and relative percent change in the risk score from the previous day; absolute and relative percent change in the risk score from the start of the trend; and number of days with a trend in the risk score. In the derivation set, we determined which trend indicators were associated with time to death in hospital, independent of the existing covariates. In the validation set, we compared the predictive performance of the existing model with and without the trend indicators.</p> <p>Results</p> <p>Three trend indicators were independently associated with time to hospital mortality: the absolute change in the risk score from the previous day; the absolute change in the risk score from the start of the trend; and the number of consecutive days with a trend in the risk score. However, adding these trend indicators to the existing model resulted in only small improvements in model discrimination and calibration.</p> <p>Conclusions</p> <p>We produced several indicators of trend in patient risk that were significantly associated with time to hospital death independent of the model used to create them. In other survival models, our approach of incorporating risk trends could be explored to improve their performance without the collection of additional data.</p

The Psychometric Properties of a Self-Administered, Open-Source Module for Valuing Metastatic Epidural Spinal Cord Compression Utilities.

INTRODUCTION: Web surveys are often used for utility valuation. Typically, custom utility valuation tools that have not undergone psychometric evaluation are used. OBJECTIVES: This study aimed to determine the psychometric properties of a metastatic epidural spinal cord compression (MESCC) module run on a customizable open-source, internet-based, self-directed utility valuation platform (Self-directed Online Assessment of Preferences [SOAP]). METHODS: Individuals accompanying patients to the emergency department waiting room in Ottawa, Canada, were recruited. Participants made SOAP MESCC health state valuations in the waiting room and 48 h later at home. Validity, agreement reliability, and responsiveness were measured by logical consistency of responses, smallest detectable change, the interclass correlation coefficient, and Guyatt\u27s responsiveness index, respectively. RESULTS: Of 285 participants who completed utility valuations, only 113 (39.6%) completed the re-test. Of these 113 participants, 92 (81.4%) provided valid responses on the first test and 75 (66.4%) provided valid responses on the test and re-test. Agreement for all groups of health states was adequate, since their smallest detectable change was less than the minimal clinically important difference. The mean interclass correlation coefficients for all health states were \u3e 0.8, indicating at least substantial reliability. Guyatt\u27s responsiveness indices all exceeded 0.80, indicating a high level of responsiveness. CONCLUSIONS: To our knowledge, this is the first validated open-source, web-based, self-directed utility valuation module. We have demonstrated the SOAP MESCC module is valid, reproducible, and responsive for obtaining ex ante utilities. Considering the successful psychometric validation of the SOAP MESCC module, other investigators can consider developing modules for other diseases where direct utility valuation is needed

The Procedural Index for Mortality Risk (PIMR): an index calculated using administrative data to quantify the independent influence of procedures on risk of hospital death

<p>Abstract</p> <p>Background</p> <p>Surgeries and other procedures can influence the risk of death in hospital. All published scales that predict post-operative death risk require clinical data and cannot be measured using administrative data alone. This study derived and internally validated an index that can be calculated using administrative data to quantify the independent risk of hospital death after a procedure.</p> <p>Methods</p> <p>For all patients admitted to a single academic centre between 2004 and 2009, we estimated the risk of all-cause death using the Kaiser Permanente Inpatient Risk Adjustment Methodology (KP-IRAM). We determined whether each patient underwent one of 503 commonly performed therapeutic procedures using Canadian Classification of Interventions codes and whether each procedure was emergent or elective. Multivariate logistic regression modeling was used to measure the association of each procedure-urgency combination with death in hospital independent of the KP-IRAM risk of death. The final model was modified into a scoring system to quantify the independent influence each procedure had on the risk of death in hospital.</p> <p>Results</p> <p>275 460 hospitalizations were included (137,730 derivation, 137,730 validation). In the derivation group, the median expected risk of death was 0.1% (IQR 0.01%-1.4%) with 4013 (2.9%) dying during the hospitalization. 56 distinct procedure-urgency combinations entered our final model resulting in a Procedural Index for Mortality Rating (PIMR) score values ranging from -7 to +11. In the validation group, the PIMR score significantly predicted the risk of death by itself (c-statistic 67.3%, 95% CI 66.6-68.0%) and when added to the KP-IRAM model (c-index improved significantly from 0.929 to 0.938).</p> <p>Conclusions</p> <p>We derived and internally validated an index that uses administrative data to quantify the independent association of a broad range of therapeutic procedures with risk of death in hospital. This scale will improve risk adjustment when administrative data are used for analyses.</p

Positive Predictive Value of Primary Subarachnoid Hemorrhage Diagnoses on Death Certificates

Purpose: Epidemiological studies of primary subarachnoid hemorrhage (pSAH) frequently include population-based death registries for case finding. The positive predictive value of pSAH diagnoses in death registries is unknown.Methods: This cross-sectional study identified all people in Ontario, Canada with pSAH listed as a cause of death between 2013 and 2017. pSAH was classified as “very likely” if diagnosis of pSAH was confirmed by autopsy, there was a previous hospitalization where pSAH probability exceeded 85% or death was preceded within a week by an emergency room visit where pSAH probability exceeded 25%. pSAH was classified as “very unlikely” if previous cerebrovascular imaging had never been done. Remaining cases were classified as “pSAH status unknown”.Results: 1,613 deaths attributed to pSAH were identified (mean 322/year). pSAH classification frequencies were as follows: very likely 528 (32.7%); very unlikely 433 (26.8%); and status unknown 652 (40.4%).Conclusion: We found that a quarter of pSAH cases in our province’s death registry were very unlikely to be true pSAH while 40% had unknown veracity. These data should be considered when using death registries for pSAH case finding