Comparison of Elixhauser and Charlson Methods for Discriminative Performance in Mortality Risk in Patients with Schizophrenic Disorders

A Comparison of the Charlson and Elixhauser Methods for Predicting Nursing Indicators in Gastrectomy with Gastric Cancer Patients

Background: Comorbidity indices such as Charlson's (CCI) and Elixhauser's (ECI) are used to adjust the patient's care, depending on the severity of their condition. However, no study has compared these indices' ability to predict nursing-sensitive outcomes (NSOs). We compared the performance of CCI and ECI in predicting NSOs in gastric cancer patients' gastrectomy. Methods: Gastric cancer patients with gastrectomy, aged 19 years or older and admitted between 2015 and 2016, were selected from the Korea Insurance Review and Assessment Service database. We examined the relationships between NSOs and CCI or ECI while adjusting patient and hospital characteristics with logistic regression. Results: The ECI item model was the best in view of the C-statistic and Akaike Information Criterion for total NSO, physiologic/metabolic derangement, and deep vein thrombosis, while the Charlson item model was the best for upper gastrointestinal tract bleeding. For the C-statistic, the ECI item model was the best for in-hospital mortality, CNS complications, shock/cardiac arrest, urinary tract infection, pulmonary failure, and wound infection, while the CCI item model was the best for hospital-acquired pneumonia and pressure ulcers. Conclusions: In predicting 8 of 11 NSOs, the ECI item model outperformed the others. For other NSOs, the best model varies between the ECI item and CCI item model.

Comparison of Three Comorbidity Measures for Predicting In-Hospital Death through a Clinical Administrative Nacional Database

BACKGROUND

Various authors have validated scales to measure comorbidity. However, the prognosis capacity variation according to the comorbidity measurement index used needs to be determined in order to identify which is the best predictor.

AIMS

To quantify the differences between the Charlson (CCI), Elixhauser (ECI) and van Walraven (WCI) comorbidity indices as prognostic factors for in-hospital mortality and to identify the best comorbidity measure predictor.

METHODS

A retrospective observational study that included all hospitalizations of patients over 18 years of age, discharged between 2017 and 2021 in the hospital, using the Minimum Basic Data Set (MBDS). We calculated CCI, ECI, WCI according to ICD-10 coding algorithms. The correlation and concordance between the three indices were evaluated by Spearman's rho and Intraclass Correlation Coefficient (ICC), respectively. The logistic regression model for each index was built for predicting in-hospital mortality. Finally, we used the receiver operating characteristic (ROC) curve for comparing the performance of each index in predicting in-hospital mortality, and the Delong method was employed to test the statistical significance of differences.

RESULTS

We studied 79,425 admission episodes. The 54.29% were men. The median age was 72 years (interquartile range [IQR]: 56-80) and in-hospital mortality rate was 4.47%. The median of ECI was = 2 (IQR: 1-4), ICW was 4 (IQR: 0-12) and ICC was 1 (IQR: 0-3). The correlation was moderate: ECI vs. WCI rho = 0.645, p < 0.001; ECI vs. CCI rho = 0.721, p < 0.001; and CCI vs. WCI rho = 0.704, p < 0.001; and the concordance was fair to good: ECI vs. WCI Intraclass Correlation Coefficient type A (ICC_A) = 0.675 (CI 95% 0.665-0.684) p < 0.001; ECI vs. CCI ICC_A = 0.797 (CI 95% 0.780-0.812), p < 0.001; and CCI vs. WCI ICC_A = 0.731 (CI 95% 0.667-0.779), p < 0.001. The multivariate regression analysis demonstrated that comorbidity increased the risk of in-hospital mortality, with differences depending on the comorbidity measurement scale: odds ratio [OR] = 2.10 (95% confidence interval [95% CI] 2.00-2.20) p > |z| < 0 using ECI; OR = 2.31 (CI 95% 2.21-2.41) p > |z| < 0 for WCI; and OR = 2.53 (CI 95% 2.40-2.67) p > |z| < 0 employing CCI. The area under the curve [AUC] = 0.714 (CI 95% 0.706-0.721) using as a predictor of in-hospital mortality CCI, AUC = 0.729 (CI 95% 0.721-0.737) for ECI and AUC = 0.750 (CI 95% 0.743-0.758) using WCI, with statistical significance (p < 0.001).

CONCLUSION

Comorbidity plays an important role as a predictor of in-hospital mortality, with differences depending on the measurement scale used, the van Walraven comorbidity index being the best predictor of in-hospital mortality.

Association of Placenta Previa With Severe Maternal Morbidity Among Patients With Placenta Accreta Spectrum Disorder

IMPORTANCE

Placenta previa is widely acknowledged as a risk factor for placenta accreta spectrum (PAS) disorders, which are severe maternal complications; however, data are limited regarding whether placenta previa is associated with a higher risk of worse maternal outcomes among patients with PAS disorders.

OBJECTIVE

To examine the association between placenta previa and the risk of severe maternal morbidities (SMMs) and higher resource use among patients with PAS disorders.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study extracted records of 3793 patients with PAS diagnosis and delivery indicators between October 1, 2015, and December 31, 2019, from the US National Inpatient Sample database.

EXPOSURES

Placenta previa.

MAIN OUTCOMES AND MEASURES

Data on 21 Centers for Disease Control and Prevention-defined SMMs and 25 study-defined surgical morbidities associated with PAS were extracted. Six surgical procedures (cystoscopy, intra-arterial balloon occlusion, cesarean delivery, hysterectomy, cystectomy, and oophorectomy), hospital length of stay, and inpatient costs were compared. Multivariable Poisson regression models built in the generalized estimating equation framework were used.

RESULTS

Among 3793 patients with PAS (median [IQR] age at admission, 33 [29-37] years), 621 women (16.4%) were Black, 765 (20.2%) were Hispanic, 1779 (46.9%) were White, 441 (11.6%) were of other races and/or ethnicities (47 [1.2%] were American Indian, 220 [5.8%] were Asian or Pacific Islander, and 174 [4.6%] were of multiple or other races and/or ethnicities), and 187 (4.9%) were of unknown race and ethnicity. A total of 1323 patients (34.9%) had placenta previa and 2470 patients (65.1%) did not; of those with placenta previa, 405 patients (30.6%) had invasive PAS. Patients with vs without placenta previa had a significantly higher rate and risk of any SMM (935 women [70.7%] vs 1087 women [44.0%]; P < .001; adjusted risk ratio [aRR], 1.19; 95% CI, 1.12-1.27) and any surgical morbidity (1170 women [88.4%] vs 1667 women [67.5%]; P < .001; aRR, 1.18; 95% CI, 1.13-1.23). With regard to specific outcomes, those with vs without placenta previa had a significantly higher rate of peripartum hemorrhage (878 patients [66.4%] vs 1217 patients [49.3%]; P < .001), blood product transfusion (413 patients [31.2%] vs 610 patients [24.7%]; P < .001), shock (83 patients [6.3%] vs 108 patients [4.4%]; P = .01), disseminated intravascular coagulation or other coagulopathy (77 patients [5.8%] vs 105 patients [4.3%]; P = .04), and urinary tract injury (44 patients [3.3%] vs 41 patients [1.7%]; P = .002). Patients with vs without placenta previa were more likely to undergo cesarean delivery (1292 patients [97.7%] vs 1787 patients [72.3%]; P < .001), hysterectomy (786 patients [59.4%] vs 689 patients [27.9%]; P < .001), cystoscopy (301 patients [22.8%] vs 203 patients [8.2%]; P < .001), cystectomy (157 patients [11.9%] vs 98 patients [4.0%]; P < .001), and intra-arterial balloon occlusion (121 patients [9.1%] vs 77 patients [3.1%]; P < .001) and to have significantly longer hospital length of stay (median [IQR], 5 [4-11] days vs 3 [3-5] days; P < .001) and total inpatient costs (median [IQR], $17 496 [$10 863-$30 619] vs $9728 [$6130-$16 790]; P < .001). Hypertensive disorder of pregnancy was associated with a decreased risk of placenta previa (aRR, 0.67; 95% CI, 0.46-0.96) among patients with PAS.

CONCLUSIONS AND RELEVANCE

In this study, placenta previa was associated with an increased risk of maternal and surgical morbidities and higher resource use among women with PAS. These findings suggest that interventions to alleviate maternal and surgical morbidities are especially needed for patients with placenta previa-complicated PAS disorders.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Comorbidity combinations in schizophrenia inpatients and their associations with service utilization: A medical record-based analysis using association rule mining

BACKGROUND

Comorbidities are common among patients with schizophrenia yet the prevalence of comorbidity combinations and their associations with inpatient service utilization and readmission have been scarcely explored.

METHODS

Data were extracted from discharge summaries of patients whose primary diagnosis was schizophrenia spectrum disorders (ICD-10: F20-F29). We identified 30 most frequent comorbidities in patients' secondary diagnoses and then used the association rule mining (ARM) method to derive comorbidity combinations associated with length of stay (LOS), daily expense and one-year readmission.

RESULTS

The study included data from 8252 patients. The top five most common comorbidities were extrapyramidal syndrome (EPS, 44.58%), constipation (31.63%), common cold (21.80%), hyperlipidemia (20.99%) and tachycardia (19.13%). Most comorbidity combinations identified by ARM were significantly associated with longer LOS (≥70 days), few were associated with higher daily expenses, and fewer with readmission. The 3-way combination of common cold, hyperlipidemia and fatty liver had the strongest association with longer LOS (adjusted OR (aOR): 3.38, 95% CI: 2.12-5.38). The combination of EPS and mild cognitive disorder was associated with higher daily expense (≥700 RMB) (aOR: 1.67, 95% CI: 1.20-2.31). The combination of constipation, tachycardia and fatty liver were associated with higher 1-year readmission (aOR: 2.05, 95% CI: 1.03-4.09).

CONCLUSION

EPS, constipation, and tachycardia were among the most commonly reported comorbidities in schizophrenia patients in Beijing, China. Specific groups of comorbidities may contribute to higher inpatient psychiatric service utilization and readmission. The mechanism behind the associations and potential interventions to optimize service use warrant further investigation.

Performance of Charlson and Elixhauser Comorbidity Index to Predict in-Hospital Mortality in Patients with Stroke in Sumadija and Western Serbia

BACKGROUND

Comorbidities are major predictors of in-hospital mortality in stroke patients. The Charlson comorbidity index (CCI) and the Elikhauser comorbidity index (ECI) are scoring systems for classifying comorbidities. We aimed to compare the performance of the CCI and ECI to predict in-hospital mortality in stroke patients.

METHODS

We included patients hospitalized for stroke in the Clinical Center of Kragujevac, Serbia for the last 7 years. Hospitalizations caused by stroke, were identified by the International Classification of Diseases-10 (ICD-10) codes I60.0 - I69.9. All patients were divided into two cohorts: Alive cohort (n=3297) and Mortality cohort (n=978).

RESULTS

There were significant associations between higher CCIS and increased risk of in-hospital mortality (HR = 1.07, 95% CI = 1.01-1.12) and between higher ECIS and increased risk of in-hospital mortality (HR = 1.04, 95% CI = 0.99-1.09). Almost 2/3 patients (66.9%) had comorbidities included in the CCI score and 1/3 patients (30.2%) had comorbidities included in the ECI score. The statistically significant higher CCI score (t = -3.88, df = 1017.96, P <0.01) and ECI score (t = -6.7, df = 1447.32, P <0.01) was in the mortality cohort.Area Under the Curve for ECI score was 0.606 and for CCI score was 0.549.

CONCLUSION

Both, the CCI and the ECI can be used as scoring systems for classifying comorbidities in the administrative databases, but the model's ECI Score had a better discriminative performance of in-hospital mortality in the stroke patients than the CCI Score model.

Identification of Functional Limitations and Discharge Destination in Patients With COVID-19

OBJECTIVES

The objectives of this study were to identify functional limitations in patients with coronavirus 2019 (COVID-19) admitted to acute care hospitals; to evaluate functional limitations by demographic, medical, and encounter characteristics; and to examine functional limitations in relation to discharge destination.

DESIGN

and Setting:This is a cross-sectional, retrospective study of adult patients with COVID-19 who were discharged from 2 different types of hospitals (academic medical center and a community hospital) within 1 health care system from January 1 to April 30, 2020.

PARTICIPANTS

Patients were identified from the Cedars-Sinai COVID-19 data registry who had a new-onset positive test for severe acute respiratory syndrome coronavirus 2. A total of 273 patients were identified, which included 230 patients who were discharged alive and 43 patients who died and were excluded from the study sample.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Functional limitations in patients with COVID-19 in acute care hospitals and the predictors for discharge disposition.

RESULTS

A total of 230 records were analyzed including demographic, encounter, medical, and functional variables. In a propensity score-matched cohort based on age and comorbidity, 88.2% had functional physical health deficits, 72.5% had functional mental health deficits, and 17.6% experienced sensory deficits. In the matched cohort, individuals discharged to an institution experienced greater physical (62.7% vs 25.5%, P<.001) and mental health (49.0% vs 23.5%, P=.006) deficits than patients discharged home. Marital status (odds ratio, 3.17; P=.011) and physical function deficits (odds ratio, 3.63; P=.025) were associated with an increase odds ratio of discharge to an institution.

CONCLUSIONS

This research highlights that functional status is a strong predictor for discharge destination to an institution for patients with COVID-19. Patients who were older, in the acute care hospital longer, and with comorbidities were more likely to be discharged to an institution. Rehabilitation is a significant aspect of the health care system for these vulnerable patients. The challenges of adjusting the role of rehabilitation providers and systems during the pandemic needs further exploration. Moreover, additional research is needed to look more closely at the many facets and timing of functional status needs, to shed light in use of interdisciplinary rehabilitation services, and to guide providers and health care systems in facilitating optimal recovery and patient outcomes.

Combining Charlson and Elixhauser scores with varying lookback predicated mortality better than using individual scores

OBJECTIVE

To investigate variation in the presence of secondary diagnosis codes in Charlson and Elixhauser comorbidity scores and assess whether including a 1-year lookback period improved prognostic adjustment by these scores individually, and combined, for 30-day mortality.

STUDY DESIGN AND SETTING

We analyzed inpatient admissions from January 1, 2007 to May 18, 2018 in Oxfordshire, UK. Comorbidity scores were calculated using secondary diagnostic codes in the diagnostic-dominant episode, and primary and secondary codes from the year before. Associations between scores and 30-day mortality were investigated using Cox models with natural cubic splines for nonlinearity, assessing fit using Akaike Information Criteria.

RESULTS

The 1-year lookback improved model fit for Charlson and Elixhauser scores vs. using diagnostic-dominant methods. Including both, and allowing nonlinearity, improved model fit further. The diagnosis-dominant Charlson score and Elixhauser score using a 1-year lookback, and their interaction, provided the best comorbidity adjustment (reduction in AIC: 761 from best single score model).

CONCLUSION

The Charlson and Elixhauser score calculated using primary and secondary diagnostic codes from 1-year lookback with secondary diagnostic codes from the current episode improved individual predictive ability. Ideally, comorbidities should be adjusted for using both the Charlson (diagnostic-dominant) and Elixhauser (1-year lookback) scores, incorporating nonlinearity and interactions for optimal confounding control.