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Nonelective Rehospitalizations and Postdischarge Mortality: Predictive Models Suitable for Use in Real Time.

ABSTRACT: Hospital discharge planning has been hampered by the lack of predictive models.To develop predictive models for nonelective rehospitalization and postdischarge mortality suitable for use in commercially available electronic medical records (EMRs).Retrospective cohort study using split validation.Integrated health care delivery system serving 3.9 million members.A total of 360,036 surviving adults who experienced 609,393 overnight hospitalizations at 21 hospitals between June 1, 2010 and December 31, 2013.A composite outcome (nonelective rehospitalization and/or death within 7 or 30 days of discharge).Nonelective rehospitalization rates at 7 and 30 days were 5.8% and 12.4%; mortality rates were 1.3% and 3.7%; and composite outcome rates were 6.3% and 14.9%, respectively. Using data from a comprehensive EMR, we developed 4 models that can generate risk estimates for risk of the combined outcome within 7 or 30 days, either at the time of admission or at 8 AM on the day of discharge. The best was the 30-day discharge day model, which had a c-statistic of 0.756 (95% confidence interval, 0.754-0.756) and a Nagelkerke pseudo-R of 0.174 (0.171-0.178) in the validation dataset. The most important predictors-a composite acute physiology score and end of life care directives-accounted for 54% of the predictive ability of the 30-day model. Incorporation of diagnoses (not reliably available for real-time use) did not improve model performance.It is possible to develop robust predictive models, suitable for use in real time with commercially available EMRs, for nonelective rehospitalization and postdischarge mortality.

SUBMITTER: Escobar GJ

PROVIDER: S-EPMC4605276 | biostudies-other | 2015 Nov

REPOSITORIES: biostudies-other

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Nonelective Rehospitalizations and Postdischarge Mortality: Predictive Models Suitable for Use in Real Time.

Escobar Gabriel J GJ Ragins Arona A Scheirer Peter P Liu Vincent V Robles Jay J Kipnis Patricia P

Medical care 20151101 11

<h4>Background</h4>Hospital discharge planning has been hampered by the lack of predictive models.<h4>Objective</h4>To develop predictive models for nonelective rehospitalization and postdischarge mortality suitable for use in commercially available electronic medical records (EMRs).<h4>Design</h4>Retrospective cohort study using split validation.<h4>Setting</h4>Integrated health care delivery system serving 3.9 million members.<h4>Participants</h4>A total of 360,036 surviving adults who experie ...[more]

PMID: 26465120

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Project description:There is an increasing need for quantitative technologies suitable for molecular detection in a variety of settings for applications including food traceability and monitoring of genetically modified (GM) crops and their products through the food processing chain. Conventional molecular diagnostics utilising real-time polymerase chain reaction (RT-PCR) and fluorescence-based determination of amplification require temperature cycling and relatively complex optics. In contrast, isothermal amplification coupled to a bioluminescent output produced in real-time (BART) occurs at a constant temperature and only requires a simple light detection and integration device.Loop mediated isothermal amplification (LAMP) shows robustness to sample-derived inhibitors. Here we show the applicability of coupled LAMP and BART reactions (LAMP-BART) for determination of genetically modified (GM) maize target DNA at low levels of contamination (0.1-5.0% GM) using certified reference material, and compare this to RT-PCR. Results show that conventional DNA extraction methods developed for PCR may not be optimal for LAMP-BART quantification. Additionally, we demonstrate that LAMP is more tolerant to plant sample-derived inhibitors, and show this can be exploited to develop rapid extraction techniques suitable for simple field-based qualitative tests for GM status determination. We also assess the effect of total DNA assay load on LAMP-BART quantitation.LAMP-BART is an effective and sensitive technique for GM detection with significant potential for quantification even at low levels of contamination and in samples derived from crops such as maize with a large genome size. The resilience of LAMP-BART to acidic polysaccharides makes it well suited to rapid sample preparation techniques and hence to both high throughput laboratory settings and to portable GM detection applications. The impact of the plant sample matrix and genome loading within a reaction must be controlled to ensure quantification at low target concentrations.

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Dataset Information

Nonelective Rehospitalizations and Postdischarge Mortality: Predictive Models Suitable for Use in Real Time.

Publications

Nonelective Rehospitalizations and Postdischarge Mortality: Predictive Models Suitable for Use in Real Time.

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