Iatrogenic risk factors associated with hospital readmission of elderly patients: A matched case-control study using a clinical data warehouse

WHAT IS KNOWN

Hospital readmission within 30 days of patient discharge has become a standard to judge the quality of hospitalizations. It is estimated that 14% of the elderly, people over 75 years old or those over 65 with comorbidities, are at risk of readmission, of which 23% are avoidable. It may be possible to identify elderly patients at risk of readmission and implement steps to reduce avoidable readmissions.

OBJECTIVE

The aim of this study was to identify iatrogenic risk factors for readmission. The secondary objective was to evaluate the rate of drug-related readmissions (DRRs) among all readmissions and compare it to the rate of readmissions for other reasons.

METHODS

We conducted a retrospective, matched, case-control study to identify non-demographic risk factors for avoidable readmission, specifically DRRs. The study included patients hospitalized between 1 September 2014 and 31 October 2015 in an 800-bed university hospital. We included patients aged 75 and over. Cases consisted of patients readmitted to the emergency department within 30 days of initial discharge. Controls did not return to the emergency department within 30 days. Cases and controls were matched on sex and age because they are known as readmissions risk factors. After comparison of the mean or percentage between cases and controls for each variable, we conducted a conditional logistic regression.

RESULTS

The risk factors identified were an emergency admission at the index hospitalization, returning home after discharge, a history of unplanned readmissions and prescription of nervous system drugs. Otherwise, 11.4% of the readmissions were DRRs, of which 30% were caused by an overdose of antihypertensive. The number of drugs at readmission was higher, and potentially inappropriate medications were more widely prescribed for DRRs than for readmissions for other reasons.

WHAT IS NEW AND CONCLUSION

In this matched case-control retrospective study, after controlling for gender and age, we identified the typical profile of elderly patients at risk of readmission. These patients had an unplanned admission at the index hospitalization and prescribed nervous system drugs at discharge from the index admission; they have a history of unplanned readmission within 30 days and return home after discharge.

Development of an Automatic Coding System for Digestive Endoscopies

Digestive endoscopies, along with all medical procedures in France are coded with the CCAM. This task is done by the physicians, is time-consuming and requires a good knowledge of the terminology besides a medical knowledge. This method offers an automatic coding of endoscopic procedures from free-text reports. Thanks to a supervised learning method, the reports are coded with an average precision and recall of 0.92 on a 1639 texts corpus.

Induction of resident memory T cells enhances the efficacy of cancer vaccine

Tissue-resident memory T cells (Trm) represent a new subset of long-lived memory T cells that remain in tissue and do not recirculate. Although they are considered as early immune effectors in infectious diseases, their role in cancer immunosurveillance remains unknown. In a preclinical model of head and neck cancer, we show that intranasal vaccination with a mucosal vector, the B subunit of Shiga toxin, induces local Trm and inhibits tumour growth. As Trm do not recirculate, we demonstrate their crucial role in the efficacy of cancer vaccine with parabiosis experiments. Blockade of TFGβ decreases the induction of Trm after mucosal vaccine immunization, resulting in the lower efficacy of cancer vaccine. In order to extrapolate this role of Trm in humans, we show that the number of Trm correlates with a better overall survival in lung cancer in multivariate analysis. The induction of Trm may represent a new surrogate biomarker for the efficacy of cancer vaccine. This study also argues for the development of vaccine strategies designed to elicit them.

Constructing a Pre-Emptive System Based on a Multidimentional Matrix and Autocompletion to Improve Diagnostic Coding in Acute Care Hospitals

Short-stay MSO (Medicine, Surgery, Obstetrics) hospitalization activities in public and private hospitals providing public services are funded through charges for the services provided (T2A in French). Coding must be well matched to the severity of the patient's condition, to ensure that appropriate funding is provided to the hospital. We propose the use of an autocompletion process and multidimensional matrix, to help physicians to improve the expression of information and to optimize clinical coding. With this approach, physicians without knowledge of the encoding rules begin from a rough concept, which is gradually refined through semantic proximity and uses information on the associated codes stemming of optimized knowledge bases of diagnosis code.