Epilepsy hospitalizations and mental disorders: A Portuguese population-based observational retrospective study (2008-2015)

BACKGROUND

Psychiatric comorbidities are highly frequent in people with epilepsy and were found to be markers of poorer prognosis. These comorbidities increase the use of healthcare resources, including emergency department visits and inpatient care. Despite this, there is little information on healthcare utilization associated with a wide range of comorbid mental disorders in people with epilepsy (PWE).

OBJECTIVE

To characterize registered mental disorders among all hospitalizations with a primary diagnosis of epilepsy and to analyze their association with crucial hospitalization outcomes.

METHODS

An observational retrospective study was performed using administrative data from hospitalization episodes with epilepsy as the primary diagnosis discharged between 2008 and 2015. Mental disorder categories 650 to 670 from Clinical Classification Software were selected as secondary diagnoses. Mann-Whitney U, Kruskall-Wallis, and Chi-squared tests were used to establish comparisons. For each episode, data regarding hospitalization outcomes was retrieved, including length of stay (LoS), in-hospital mortality (IHM), 8-year period readmissions, and total estimated charges.

RESULTS

Overall, 27,785 hospitalizations were analyzed and 33.9% had registered mental disorders, with alcohol-related disorders being the most prevalent (11.7%). For episodes with a concomitant register of a mental disorder, LoS was significantly longer (5.0 vs. 4.0 days, P <0.001), and IHM was higher (2.8% vs. 2.2%, P <0.001), as were readmissions (25.5% vs. 23.7%, P <0.001), and median episodes' charges (1,578.7 vs. 1,324.4 euros, P <0.001).

CONCLUSION

Epilepsy-related hospitalizations with registered mental disorders heightened the utilization of healthcare resources, stressing the importance of diagnosing and treating mental disorders in PWE.

Concomitant Hiatal Hernia Repair with Sleeve Gastrectomy: A 5-Year Analysis

Background: The sleeve gastrectomy (SG) can be associated with postoperative gastroesophageal reflux and when a hiatal hernia (HH) is present, it should be fixed. Earlier studies have shown that 20% of SG have a concomitant hiatal hernia repair (SG+HHR). The aim of this project is to determine the rate of SG+HHR in a large state administrative database.

Methods: The Texas Inpatient Public Use Data File (IPUDF) and Outpatient Public Use Data File (OPUDF) for the years 2013-2017 were examined for patients that underwent SG+HHR at the same time. Patient demographics, diagnosis, and charge data were also examined. A t-test was performed between groups and P was considered significant at < 0.05.

Results: In the OPUDF, there were 6,193 (33.7%) patients who underwent SG+HHR out of 18,403 patients who underwent SG. Mean charges were $94,741 [standard deviation (SD) = $87,284]. Length of stay (LOS) was 2.1 (SD = 3.5) vs 2.3 days (SD = 3.3) with a shorter stay for SG+HHR vs SG alone (P < 0.001). In the IPUDF, there were 11,536 (21.1%) patients who underwent SG+HHR out of 54,545 patients who underwent SG. Mean charges were $69,006 (SD = $46,365). LOS was 1.59 days (SD = 3.7) for SG+HHR vs 1.63 days (SD = 1.6) for SG (P = .043). The rate of SG+HHR increased over the study period.

Conclusions: SG+HHR is common in both the outpatient and inpatient setting. There is a yearly trend of increasing rates of SG+HHR.

Creation and Validation of a Stroke Scale to Increase Utility of National Inpatient Sample Administrative Data for Clinical Stroke Research

INTRODUCTION

The National Inpatient Sample (NIS) has led to several breakthroughs via large sample size. However, utility of NIS is limited by the lack of admission NIHSS and 90-day modified Rankin score (mRS). This study creates estimates for stroke severity at admission and 90-day mRS using NIS data for acute ischemic stroke (AIS) patients treated with mechanical thrombectomy (MT).

METHODS

Three patient cohorts undergoing MT for AIS were utilized: Cohort 1 (N = 3729) and Cohort 3 (N = 1642) were derived from NIS data. Cohort 2 (N=293) was derived from a prospectively-maintained clinical registry. Using Cohort 1, Administrative Stroke Outcome Variable (ASOV) was created using disposition and mortality. Factors reflective of stroke severity were entered into a stepwise logistic regression predicting poor ASOV. Odds ratios were used to create the Administrative Data Stroke Scale (ADSS). Performances of ADSS and ASOV were tested using Cohort 2 and compared with admission NIHSS and 90-day mRS, respectively. ADSS performance was compared with All Patient Refined-Diagnosis Related Group (APR-DRG) severity score using Cohort 3.

RESULTS

Agreement of ASOV with 90-day mRS > 2 was fair (κ = 0.473). Agreement with 90-day mRS > 3 was substantial (κ = 0.687). ADSS significantly correlated (p < 0.001) with clinically-significant admission NIHSS > 15. ADSS performed comparably (AUC = 0.749) to admission NIHSS (AUC = 0.697) in predicting 90-day mRS > 2 and mRS > 3 (AUC = 0.767, 0.685, respectively). ADSS outperformed APR-DRG severity score in predicting poor ASOV (AUC = 0.698, 0.682, respectively).

CONCLUSION

We developed and validated measures of stroke severity at admission (ADSS) and outcome (ASOV, estimate for 90-day mRS > 3) to increase utility of NIS data in stroke research.

Accuracy of colorectal cancer ICD-9-CM codes in Italian administrative healthcare databases: a cross-sectional diagnostic study

Objectives To assess the accuracy of International Classification of Diseases, Ninth Revision - Clinical Modification (ICD-9-CM) codes in identifying subjects with colorectal cancer.

DESIGN

A diagnostic accuracy study comparing ICD-9-CM codes (index test) for colorectal cancers with medical chart (as a reference standard). Case ascertainment based on neoplastic lesion(s) within the colon/rectum and histological documentation from a primary or metastatic site positive for colorectal cancer.

SETTING

Administrative databases from the Umbria region, Azienda Sanitaria Locale (ASL) Napoli 3 Sud (NA) region and Friuli Venezia Giulia (FVG) region.

PARTICIPANTS

We randomly selected 130 incident patients from each hospital discharge database, admitted between 2012 and 2014, having colorectal cancer ICD-9 codes located in primary position, and 94 non-cases, that is, patients having a diagnosis of cancer (ICD-9 140-239) other than colorectal cancer in primary position.

OUTCOME MEASURES

Sensitivity, specificity and predictive values for 153.x code (colon cancer) and for 154.x code (rectal cancer).

RESULTS

The positive predictive value (PPV) for colon cancer diagnoses was 80% for Umbria (95% CI 73% to 87%), 81% for NA (95% CI 73% to 88%) and 80% for FVG (95% CI 72% to 87%).The sensitivity ranged from 98% to 99%, while the specificity ranged from 78% to 80% in the three units.For rectal cancer, the PPV was 84% for Umbria (95% CI 77% to 90%), 80% for NA (95% CI 72% to 87%) and 81% for FVG (95% CI 73% to 87%). The sensitivities ranged from 98% to 100%, while the specificity estimates from 79% to 82%.

CONCLUSIONS

Administrative databases in Italy can be a valuable tool for cancer surveillance as well as monitoring geographical and temporal variation of cancer practice.

Wisdom within: unlocking the potential of big data for nursing regulators

AIM

This paper explores the potential for incorporating big data in nursing regulators' decision-making and policy development. Big data, commonly described as the extensive volume of information that individuals and agencies generate daily, is a concept familiar to the business community but is only beginning to be explored by the public sector.

BACKGROUND

Using insights gained from a recent research project, the College and Association of Registered Nurses of Alberta, in Canada is creating an organizational culture of data-driven decision-making throughout its regulatory and professional functions. The goal is to enable the organization to respond quickly and profoundly to nursing issues in a rapidly changing healthcare environment.

SOURCES OF EVIDENCE

The evidence includes a review of the Learning from Experience: Improving the Process of Internationally Educated Nurses' Applications for Registration (LFE) research project (2011-2016), combined with a literature review on data-driven decision-making within nursing and healthcare settings, and the incorporation of big data in the private and public sectors, primarily in North America.

DISCUSSION

This paper discusses experience and, more broadly, how data can enhance the rigour and integrity of nursing and health policy.

CONCLUSION

Nursing regulatory bodies have access to extensive data, and the opportunity to use these data to inform decision-making and policy development by investing in how it is captured, analysed and incorporated into decision-making processes.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

Understanding and using big data is a critical part of developing relevant, sound and credible policy. Rigorous collection and analysis of big data supports the integrity of the evidence used by nurse regulators in developing nursing and health policy.