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Administrative and Claims Data Help Predict Patient Mortality in Intensive Care Units by Logistic Regression: A Nationwide Database Study.

ABSTRACT: Background:Increasing attention has been paid to the predictive power of different prognostic scoring systems for decades. In this study, we compared the abilities of three commonly used scoring systems to predict short-term and long-term mortalities, with the intention of building a better prediction model for critically ill patients. We used the data from the National Health Insurance Research Database (NHIRD) in Taiwan, which included information on patient age, comorbidities, and presence of organ failure to build a new prediction model for short-term and long-term mortalities. Methods:We retrospectively collected the medical records of patients in the intensive care unit of a regional hospital in 2012 and linked them to the claims data from the NHIRD. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Elixhauser Comorbidity Index (ECI), and Charlson Comorbidity Index (CCI) were compared for their predictive abilities. Multiple logistic regression tests were performed, and the results were presented as receiver operating characteristic curves and C-statistic. Results:The APACHE II score has the best predictive power for inhospital mortality (0.79; C - statistic = 0.77 - 0.83) and 1-year mortality (0.77; C - statistic = 0.74 - 0.79). The ECI and CCI alone have poorer predictive power and need to be combined with other variables to be comparable to the APACHE II score, as predictive tools. Using CCI together with age, sex, and whether or not the patient required mechanical ventilation is estimated to have a C-statistic of 0.773 (95% CI 0.744-0.803) for inhospital mortality, 0.782 (95% CI 0.76-0.81) for 30-day mortality, and 0.78 (95% CI 0.75-0.80) for 1-year mortality. Conclusions:We present a new prognostic model that combines CCI with age, sex, and mechanical ventilation status and can predict mortality, comparable to the APACHE II score.

SUBMITTER: Hsu YT

PROVIDER: S-EPMC7061120 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Administrative and Claims Data Help Predict Patient Mortality in Intensive Care Units by Logistic Regression: A Nationwide Database Study.

Hsu Yu-Ting YT He Yi-Ting YT Ting Chien-Kun CK Tsou Mei-Yung MY Tang Gau-Jun GJ Pu Christy C Pu Christy C

BioMed research international 20200225

<h4>Background</h4>Increasing attention has been paid to the predictive power of different prognostic scoring systems for decades. In this study, we compared the abilities of three commonly used scoring systems to predict short-term and long-term mortalities, with the intention of building a better prediction model for critically ill patients. We used the data from the National Health Insurance Research Database (NHIRD) in Taiwan, which included information on patient age, comorbidities, and pre ...[more]

PMID: 32185223

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Project description:ObjectivesClinical practice guidelines for the management of pain and sedation in critically ill patients have been developed and applied; however, there is limited data on medication use among elderly patients. This study identifies current practice patterns for analgo-sedative use in mechanically ventilated elderly patients in Korea using a national claims database.DesignCross-sectional study.Setting and participantsVentilated elderly patients aged 65 years or older in intensive care units (ICUs) from an aged patients' national claims database in Korea PRIMARY OUTCOME MEASURES: Use of sedatives including benzodiazepines, opioids and non-opioid analgesics, neuromuscular blocking agents (NMBAs) and antipsychotic drugs were analysed by the duration of mechanical ventilation (MV), age and time.ResultsFrom 2012 to 2016, 22 677 elderly patients underwent MV in 267 general or tertiary ICUs. Mean age was 77.2 (±6.9) years and the median duration of MV was 4.1 days; 77.2% of patients received sedatives, 65.0% analgesics, 29.1% NMBAs and 19.6% antipsychotics. Midazolam (62.0%) was the most commonly prescribed medication. The proportions of sedatives, analgesics and NMBAs increased, whereas the percentages of person-days decreased with longer MV duration (p<0.01). With advanced age, the prevalence and duration of sedative, analgesic and NMBA use decreased (adjusted OR (95% CI) 0.98 (0.97 to 0.98) in all three classes) while antipsychotic did not (adjusted OR 1.00 (1.00-1.01)). Annually, benzodiazepines showed reduced administration (76.2% in 2012 and 71.4% in 2016, p<0.01), while daily opioid dose increased (21.6 in 2012 vs 30.0 mg in 2016, p<0.01).ConclusionsThe prevalence of sedative, analgesic and NMBAs use and daily opioid doses were lower, whereas antipsychotic use was higher compared with those in previous studies in adult patients. The findings warrant further studies investigating appropriateness and safety of medication use that consider clinical severity scores with a focus on elderly patients in ICUs.

Project description:BackgroundWe aimed to examine recent trends in patient characteristics and mortality in patients with acute kidney injury (AKI) receiving renal replacement therapy (RRT), including continuous RRT (CRRT) and intermittent RRT (IRRT), in intensive care units (ICUs).MethodsFrom the Diagnosis Procedure Combination database in Japan during 6 months (July-December) from 2007 to 2016, we identified patients with AKI who received RRT in ICUs. We restricted the study participants to those admitted to hospitals (in which both CRRT and IRRT were available) that participated in the Diagnosis Procedure Combination database for all 10 years. We examined the trends in patient characteristics and mortality overall, by RRT modality, and by main diagnosis category subgroup. Logistic regression was used to adjust for patient characteristics.ResultsWe identified 51,758 patients starting RRT in 287 hospitals, including 39,471 (76.3%) and 12,287 (23.7%) patients starting CRRT and IRRT. The crude in-hospital mortality declined from 44.9 to 36.1% (P for trend < 0.001). Compared with 2007, the adjusted odds ratio (aOR) for in-hospital mortality was 0.66 (95% confidence interval (CI) 0.60-0.72) in 2016, and the decreasing trend was observed in both patients starting CRRT (aOR 0.67, 95% CI 0.61-0.75) and IRRT (0.58, 0.45-0.74), and in all subgroups except for coronary artery disease: sepsis aOR 0.68 (95% CI 0.57-0.81); cardiovascular surgery 0.58 (0.45-0.76); coronary artery disease 0.84 (0.60-1.19); non-coronary heart disease 0.78 (0.64-0.94); central nervous system disorders 0.42 (0.28-0.62); trauma 0.39 (0.21-0.72); and other 0.64 (0.50-0.82).ConclusionsThis nationwide study confirmed a consistent decline in mortality among patients with AKI on RRT in ICUs. The adjusted mortality also declined during the study period; however, physiological variables were not measured in this study and it is possible that RRT may have been indicated for patients with less severe AKI in more recent years.

Dataset Information

Administrative and Claims Data Help Predict Patient Mortality in Intensive Care Units by Logistic Regression: A Nationwide Database Study.

Publications

Administrative and Claims Data Help Predict Patient Mortality in Intensive Care Units by Logistic Regression: A Nationwide Database Study.

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