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Královcová M, Karvunidis T, Matějovič M. Critical care for multimorbid patients. VNITRNI LEKARSTVI 2023; 69:166-172. [PMID: 37468311 DOI: 10.36290/vnl.2023.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Multimorbidity - the simultaneous presence of several chronic diseases - is very common in the critically ill patients. Its prevalence is roughly 40-85 % and continues to increase further. Certain chronic diseases such as diabetes, obesity, chronic heart, pulmonary, liver or kidney disease and malignancy are associated with higher risk of developing serious acute complications and therefore the possible need for intensive care. This review summarizes and discusses selected specifics of critical care for multimorbid patients.
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External Validation of Mortality Prediction Models for Critical Illness Reveals Preserved Discrimination but Poor Calibration. Crit Care Med 2023; 51:80-90. [PMID: 36378565 DOI: 10.1097/ccm.0000000000005712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVES In a recent scoping review, we identified 43 mortality prediction models for critically ill patients. We aimed to assess the performances of these models through external validation. DESIGN Multicenter study. SETTING External validation of models was performed in the Simple Intensive Care Studies-I (SICS-I) and the Finnish Acute Kidney Injury (FINNAKI) study. PATIENTS The SICS-I study consisted of 1,075 patients, and the FINNAKI study consisted of 2,901 critically ill patients. MEASUREMENTS AND MAIN RESULTS For each model, we assessed: 1) the original publications for the data needed for model reconstruction, 2) availability of the variables, 3) model performance in two independent cohorts, and 4) the effects of recalibration on model performance. The models were recalibrated using data of the SICS-I and subsequently validated using data of the FINNAKI study. We evaluated overall model performance using various indexes, including the (scaled) Brier score, discrimination (area under the curve of the receiver operating characteristics), calibration (intercepts and slopes), and decision curves. Eleven models (26%) could be externally validated. The Acute Physiology And Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS)-Reduced (SAPS-R)' and Simplified Mortality Score for the ICU models showed the best scaled Brier scores of 0.11' 0.10' 0.10' and 0.06' respectively. SAPS II, APACHE II, and APACHE IV discriminated best; overall discrimination of models ranged from area under the curve of the receiver operating characteristics of 0.63 (0.61-0.66) to 0.83 (0.81-0.85). We observed poor calibration in most models, which improved to at least moderate after recalibration of intercepts and slopes. The decision curve showed a positive net benefit in the 0-60% threshold probability range for APACHE IV and SAPS-R. CONCLUSIONS In only 11 out of 43 available mortality prediction models, the performance could be studied using two cohorts of critically ill patients. External validation showed that the discriminative ability of APACHE II, APACHE IV, and SAPS II was acceptable to excellent, whereas calibration was poor.
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Suls J, Salive ME, Koroukian SM, Alemi F, Silber JH, Kastenmüller G, Klabunde CN. Emerging approaches to multiple chronic condition assessment. J Am Geriatr Soc 2022; 70:2498-2507. [PMID: 35699153 PMCID: PMC9489607 DOI: 10.1111/jgs.17914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 04/25/2022] [Accepted: 05/07/2022] [Indexed: 01/01/2023]
Abstract
Older adults experience a higher prevalence of multiple chronic conditions (MCCs). Establishing the presence and pattern of MCCs in individuals or populations is important for healthcare delivery, research, and policy. This report describes four emerging approaches and discusses their potential applications for enhancing assessment, treatment, and policy for the aging population. The National Institutes of Health convened a 2-day panel workshop of experts in 2018. Four emerging models were identified by the panel, including classification and regression tree (CART), qualifying comorbidity sets (QCS), the multimorbidity index (MMI), and the application of omics to network medicine. Future research into models of multiple chronic condition assessment may improve understanding of the epidemiology, diagnosis, and treatment of older persons.
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Affiliation(s)
- Jerry Suls
- Feinstein Institutes for Medical Research/Northwell Health (previously National Cancer Institute)New York CityNew YorkUSA
| | | | | | | | | | - Gabi Kastenmüller
- Helmholtz Zentrum MünchenInstitute for Computational BiologyOberschleißheimGermany
| | - Carrie N. Klabunde
- Office of Disease PreventionNational Institutes of HealthBethesdaMarylandUSA
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Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
For patients with rare comorbidities, there are insufficient observations to accurately estimate the effectiveness of treatment. At the same time, all diagnosis, including rare diagnosis, are part of the International Classification of Disease (ICD). Grouping ICD into broader concepts (i.e., ontology adjustment) can not only increase accuracy of estimating antidepressant effectiveness for patients with rare conditions but also prevent overfitting in big data analysis. In this study, 3,678,082 depressed patients treated with antidepressants were obtained from OptumLabs® Data Warehouse (OLDW). For rare diagnoses, adjustments were made by using the likelihood ratio of the immediate broader concept in the ICD hierarchies. The accuracy of models in training (90%) and test (10%) sets was examined using the area under the receiver operating curves (AROC). The gap in training and test AROC shows how much random noise was modeled. If the gap is large, then the parameters of the model, including the reported effectiveness of the antidepressant for patients with rare conditions, are suspect. There was, on average, a 9.0% reduction in the AROC gap after using the ontological adjustment. Therefore, ontology adjustment can reduce model overfitting, leading to better parameter estimates from the training set.
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Francisco PMSB, Assumpção DD, Bacurau AGDM, Silva DSMD, Malta DC, Borim FSA. Multimorbidity and use of health services in the oldest old in Brazil. REVISTA BRASILEIRA DE EPIDEMIOLOGIA 2021; 24:e210014. [PMID: 34910068 DOI: 10.1590/1980-549720210014.supl.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 07/22/2021] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE To estimate the prevalence of multimorbidity in long-lived Brazilian individuals (age ≥80 years) and to associated it with the use of health services. METHODS Cross-sectional population-based study with data from the 2019 National Survey of Health (n=6,098). Frequencies of use of services were estimated for older people with multimorbidity and according to sex, health insurance ownership, and self-rated health. The prevalence rates, crude and adjusted prevalence ratios, and the respective 95% confidence intervals were calculated. RESULTS The average age of the older adults was 85 years and about 62% were women; the prevalence of multimorbidity was 57.1%, higher in women, in those who have health insurance, and who reside in the southern region of the country (p<0.05). In the oldest old with multimorbidity, the use of services in the last 15 days reached 64.6%, and more than 70% were hospitalized in the last year or did not carry out activities in the previous two weeks for health reasons. Differences were observed for the indicators of service use in relation to sex, health insurance ownership, and self-rated health, according to multimorbidity. CONCLUSION Indicators for the use of health services were higher in older individuals who have two or more chronic diseases, regardless of sociodemographic conditions and self-rated health, showing the impact of multimorbidity per se in determining the use of services among the oldest old.
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Affiliation(s)
| | - Daniela de Assumpção
- School of Medical Sciences, Universidade Estadual de Campinas, - Campinas (SP), Brazil
| | | | | | | | - Flávia Silva Arbex Borim
- School of Medical Sciences, Universidade Estadual de Campinas, - Campinas (SP), Brazil.,School of Health Sciences, Universidade de Brasília- Brasília (DF), Brazil
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Beil M, Flaatten H, Guidet B, Sviri S, Jung C, de Lange D, Leaver S, Fjølner J, Szczeklik W, van Heerden PV. The management of multi-morbidity in elderly patients: Ready yet for precision medicine in intensive care? CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2021; 25:330. [PMID: 34507597 PMCID: PMC8431262 DOI: 10.1186/s13054-021-03750-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/27/2021] [Indexed: 11/16/2022]
Abstract
There is ongoing demographic ageing and increasing longevity of the population, with previously devastating and often-fatal diseases now transformed into chronic conditions. This is turning multi-morbidity into a major challenge in the world of critical care. After many years of research and innovation, mainly in geriatric care, the concept of multi-morbidity now requires fine-tuning to support decision-making for patients along their whole trajectory in healthcare, including in the intensive care unit (ICU). This article will discuss current challenges and present approaches to adapt critical care services to the needs of these patients.
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Affiliation(s)
- Michael Beil
- Department of Medical Intensive Care, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Hans Flaatten
- Department of Anaesthesia and Intensive Care Medicine, Haukeland University Hospital, Bergen, Norway
| | - Bertrand Guidet
- Service de Reanimation, Hopital Saint-Antoine, Paris, France
| | - Sigal Sviri
- Department of Medical Intensive Care, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Christian Jung
- Department of Cardiology, Pulmonology and Vascular Medicine, Faculty of Medicine, Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
| | - Dylan de Lange
- Department of Intensive Care Medicine, University Medical Center, University of Utrecht, Utrecht, The Netherlands
| | - Susannah Leaver
- Department of Adult Critical Care, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Jesper Fjølner
- Department of Intensive Care, Aarhus University Hospital, Aarhus, Denmark
| | - Wojciech Szczeklik
- Center for Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Kraków, Poland
| | - Peter Vernon van Heerden
- General Intensive Care Unit, Department of Anesthesiology, Critical Care and Pain Medicine, Hadassah Medical Center and Faculty of Medicine, Hadassah University Hospital, Hebrew University of Jerusalem, Jerusalem, Israel.
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Ding X, Lian H, Wang X. Management of Very Old Patients in Intensive Care Units. Aging Dis 2021; 12:614-624. [PMID: 33815886 PMCID: PMC7990356 DOI: 10.14336/ad.2020.0914] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 09/14/2020] [Indexed: 02/06/2023] Open
Abstract
The global population is aging and the demand for critical care wards increasing. Aging is associated not only with physiological and cognitive vulnerability, but also with a decline in organ function. A new topic in geriatric care is how to appropriately use critical care resources and provide the best treatment plan for very old patients (VOPs). Our special geriatric intensive care unit has admitted nearly 500 VOPs. In this review, we share our VOP treatment strategy and summarize the key points as “ABCCDEFGHI bundles.” The aim is to help intensivists to provide more comprehensive therapy for VOPs in intensive care units.
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Affiliation(s)
- Xin Ding
- 1Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Lian
- 2Department of Health Care, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoting Wang
- 1Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,2Department of Health Care, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Factors affecting mortality in trauma patients hospitalized in the intensive care unit. JOURNAL OF SURGERY AND MEDICINE 2020. [DOI: 10.28982/josam.812409] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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Alemi F, Avramovic S, Renshaw KD, Kanchi R, Schwartz M. Relative accuracy of social and medical determinants of suicide in electronic health records. Health Serv Res 2020; 55 Suppl 2:833-840. [PMID: 32880954 PMCID: PMC7518826 DOI: 10.1111/1475-6773.13540] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 06/22/2020] [Accepted: 07/13/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE This paper compares the accuracy of predicting suicide from Social Determinants of Health (SDoH) or history of illness. POPULATION STUDIED 5 313 965 Veterans who at least had two primary care visits between 2008 and 2016. STUDY DESIGN The dependent variable was suicide or intentional self-injury. The independent variables were 10 495 International Classification of Disease (ICD) Version 9 codes, age, and gender. The ICD codes included 40 V-codes used for measuring SDoH, such as family disruption, family history of substance abuse, lack of education, legal impediments, social isolation, unemployment, and homelessness. The sample was randomly divided into training (90 percent) and validation (10 percent) sets. Area under the receiver operating characteristic (AROC) was used to measure accuracy of predictions in the validation set. PRINCIPAL FINDINGS Separate analyses were done for inpatient and outpatient codes; the results were similar. In the hospitalized group, the mean age was 67.2 years, and 92.1 percent were male. The mean number of medical diagnostic codes during the study period was 37; and 12.9 percent had at least one SDoH V-code. At least one episode of suicide or intentional self-injury occurred in 1.89 percent of cases. SDoH V-codes, on average, elevated the risk of suicide or intentional self-injury by 24-fold (ranging from 4- to 86-fold). An index of 40 SDoH codes predicted suicide or intentional self-injury with an AROC of 0.64. An index of 10 445 medical diagnoses, without SDoH V-codes, had AROC of 0.77. The combined SDoH and medical diagnoses codes also had AROC of 0.77. CONCLUSION In predicting suicide or intentional self-harm, SDoH V-codes add negligible information beyond what is already available in medical diagnosis codes. IMPLICATIONS FOR PRACTICE Policies that affect SDoH (eg, housing policies, resilience training) may not have an impact on suicide rates, if they do not change the underlying medical causes of SDoH.
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Affiliation(s)
- Farrokh Alemi
- Department of Health Administration and PolicyGeorge Mason UniversityVirginia
| | - Sanja Avramovic
- Department of Health Administration and PolicyGeorge Mason UniversityVirginia
| | | | - Rania Kanchi
- Department of Population HealthNew York UniversityNew York
| | - Mark Schwartz
- Department of Population HealthNew York UniversityNew York
- Veteran AdministrationNew York Harbor Healthcare SystemNew York
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10
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Keuning BE, Kaufmann T, Wiersema R, Granholm A, Pettilä V, Møller MH, Christiansen CF, Castela Forte J, Snieder H, Keus F, Pleijhuis RG, Horst ICC. Mortality prediction models in the adult critically ill: A scoping review. Acta Anaesthesiol Scand 2020; 64:424-442. [PMID: 31828760 DOI: 10.1111/aas.13527] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 10/07/2019] [Accepted: 12/04/2019] [Indexed: 12/24/2022]
Abstract
BACKGROUND Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients. METHODS Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication. RESULTS In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R2 (4.7%). CONCLUSIONS Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.
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Affiliation(s)
- Britt E. Keuning
- Department of Critical Care University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
| | - Thomas Kaufmann
- Department of Anesthesiology University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
| | - Renske Wiersema
- Department of Critical Care University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
| | - Anders Granholm
- Department of Intensive Care Copenhagen University Hospital Rigshospitalet, Copenhagen Denmark
| | - Ville Pettilä
- Division of Intensive Care Medicine Department of Anesthesiology, Intensive Care and Pain Medicine University of Helsinki and Helsinki University Hospital Helsinki Finland
| | - Morten Hylander Møller
- Department of Intensive Care Copenhagen University Hospital Rigshospitalet, Copenhagen Denmark
- Centre for Research in Intensive Care Copenhagen University Hospital Rigshospitalet, Copenhagen Denmark
| | | | - José Castela Forte
- Department of Critical Care University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
- Bernoulli Institute for MathematicsComputer Science and Artificial IntelligenceUniversity of Groningen Groningen The Netherlands
| | - Harold Snieder
- Department of Epidemiology University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
| | - Frederik Keus
- Department of Critical Care University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
| | - Rick G. Pleijhuis
- Department of Internal Medicine University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
| | - Iwan C. C. Horst
- Department of Critical Care University of GroningenUniversity Medical Center Groningen Groningen The Netherlands
- Department of Intensive Care Maastricht University Medical Center+Maastricht University Maastricht The Netherlands
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Zador Z, Landry A, Cusimano MD, Geifman N. Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2019; 23:247. [PMID: 31287020 PMCID: PMC6613271 DOI: 10.1186/s13054-019-2486-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 05/22/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients' medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations ("multimorbidity"). We hypothesized the existence of patient subgroups in critical care with distinct multimorbidity states. We further hypothesize that certain multimorbidity states associate with higher rates of organ failure, sepsis, and mortality co-occurring with these clinical problems. METHODS We analyzed 36,390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. We used latent class analysis to identify distinct patient subgroups based on demographics, admission type, and morbidity compositions and compared the prevalence of organ dysfunction, sepsis, and inpatient mortality for each subgroup. RESULTS We identified six clinically distinct multimorbidity subgroups labeled based on their dominant Elixhauser disease classes. The "cardiopulmonary" and "cardiac" subgroups consisted of older patients with a high prevalence of cardiopulmonary conditions and constituted 6.1% and 26.4% of study cohort respectively. The "young" subgroup included 23.5% of the cohort composed of young and healthy patients. The "hepatic/addiction" subgroup, constituting 9.8% of the cohort, consisted of middle-aged patients (mean age of 52.25, 95% CI 51.85-52.65) with the high rates of depression (20.1%), alcohol abuse (47.75%), drug abuse (18.2%), and liver failure (67%). The "complicated diabetics" and "uncomplicated diabetics" subgroups constituted 9.4% and 24.8% of the study cohort respectively. The complicated diabetics subgroup demonstrated higher rates of end-organ complications (88.3% prevalence of renal failure). Rates of organ dysfunction and sepsis ranged 19.6-69% and 12.5-46.7% respectively in the six subgroups. Mortality co-occurring with organ dysfunction and sepsis ranges was 8.4-23.8% and 11.7-27.4% respectively. These adverse outcomes were most prevalent in the hepatic/addiction subgroup. CONCLUSION We identify distinct multimorbidity states that associate with relatively higher prevalence of organ dysfunction, sepsis, and co-occurring mortality. The findings promote the incorporation of multimorbidity in healthcare models and the shift away from the current single-disease paradigm in clinical practice, training, and trial design.
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Affiliation(s)
- Zsolt Zador
- Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, ON, Canada. .,Institute of Cardiovascular Sciences, Centre for Vascular and Stroke Research, University of Manchester, Manchester, UK.
| | - Alexander Landry
- Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, ON, Canada
| | - Michael D Cusimano
- Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, Toronto, ON, Canada
| | - Nophar Geifman
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
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Nielsen AB, Thorsen-Meyer HC, Belling K, Nielsen AP, Thomas CE, Chmura PJ, Lademann M, Moseley PL, Heimann M, Dybdahl L, Spangsege L, Hulsen P, Perner A, Brunak S. Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records. LANCET DIGITAL HEALTH 2019; 1:e78-e89. [PMID: 33323232 DOI: 10.1016/s2589-7500(19)30024-x] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 04/12/2019] [Accepted: 04/15/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Intensive-care units (ICUs) treat the most critically ill patients, which is complicated by the heterogeneity of the diseases that they encounter. Severity scores based mainly on acute physiology measures collected at ICU admission are used to predict mortality, but are non-specific, and predictions for individual patients can be inaccurate. We investigated whether inclusion of long-term disease history before ICU admission improves mortality predictions. METHODS Registry data for long-term disease histories for more than 230 000 Danish ICU patients were used in a neural network to develop an ICU mortality prediction model. Long-term disease histories and acute physiology measures were aggregated to predict mortality risk for patients for whom both registry and ICU electronic patient record data were available. We compared mortality predictions with admission scores on the Simplified Acute Physiology Score (SAPS) II, the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II, and the best available multimorbidity score, the Multimorbidity Index. An external validation set from an additional hospital was acquired after model construction to confirm the validity of our model. During initial model development data were split into a training set (85%) and an independent test set (15%), and a five-fold cross-validation was done during training to avoid overfitting. Neural networks were trained for datasets with disease history of 1 month, 3 months, 6 months, 1 year, 2·5 years, 5 years, 7·5 years, 10 years, and 23 years before ICU admission. FINDINGS Mortality predictions with a model based solely on disease history outperformed the Multimorbidity Index (Matthews correlation coefficient 0·265 vs 0·065), and performed similarly to SAPS II and APACHE II (Matthews correlation coefficient with disease history, age, and sex 0·326 vs 0·347 and 0·300 for SAPS II and APACHE II, respectively). Diagnoses up to 10 years before ICU admission affected current mortality prediction. Aggregation of previous disease history and acute physiology measures in a neural network yielded the most precise predictions of in-hospital mortality (Matthews correlation coefficient 0·391 for in-hospital mortality compared with 0·347 with SAPS II and 0·300 with APACHE II). These results for the aggregated model were validated in an external independent dataset of 1528 patients (Matthews correlation coefficient for prediction of in-hospital mortality 0·341). INTERPRETATION Longitudinal disease-spectrum-wide data available before ICU admission are useful for mortality prediction. Disease history can be used to differentiate mortality risk between patients with similar vital signs with more precision than SAPS II and APACHE II scores. Machine learning models can be deconvoluted to generate novel understandings of how ICU patient features from long-term and short-term events interact with each other. Explainable machine learning models are key in clinical settings, and our results emphasise how to progress towards the transformation of advanced models into actionable, transparent, and trustworthy clinical tools. FUNDING Novo Nordisk Foundation and Innovation Fund Denmark.
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Affiliation(s)
- Annelaura B Nielsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hans-Christian Thorsen-Meyer
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Intensive Care, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Kirstine Belling
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anna P Nielsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cecilia E Thomas
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Piotr J Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Lademann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pope L Moseley
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marc Heimann
- Centre for IT, Medical Technology and Telephony Services, Capital Region of Denmark, Copenhagen, Denmark
| | | | | | | | - Anders Perner
- Department of Intensive Care, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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C-LACE2: computational risk assessment tool for 30-day post hospital discharge mortality. HEALTH AND TECHNOLOGY 2018. [DOI: 10.1007/s12553-018-0263-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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14
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Alemi F, Avramovic S, Schwartz MD. Electronic Health Record-Based Screening for Substance Abuse. BIG DATA 2018; 6:214-224. [PMID: 30283729 PMCID: PMC6154440 DOI: 10.1089/big.2018.0002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.
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Affiliation(s)
- Farrokh Alemi
- Health Informatics Program, Department of Health Administration and Policy, George Mason University, Fairfax, Virginia
- Address correspondence to: Farrokh Alemi, Health Informatics Program, Department of Health Administration and Policy, George Mason University 1J3, 4400 University Drive, Fairfax, VA 22030,
| | - Sanja Avramovic
- Health Informatics Program, Department of Health Administration and Policy, George Mason University, Fairfax, Virginia
| | - Mark D. Schwartz
- Department of Population Health, New York University School of Medicine, New York, New York
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Forman DE, Maurer MS, Boyd C, Brindis R, Salive ME, Horne FM, Bell SP, Fulmer T, Reuben DB, Zieman S, Rich MW. Multimorbidity in Older Adults With Cardiovascular Disease. J Am Coll Cardiol 2018; 71:2149-2161. [PMID: 29747836 PMCID: PMC6028235 DOI: 10.1016/j.jacc.2018.03.022] [Citation(s) in RCA: 187] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 02/26/2018] [Accepted: 03/01/2018] [Indexed: 11/19/2022]
Abstract
Multimorbidity occurs in adults of all ages, but the number and complexity of comorbid conditions commonly increase with advancing age such that cardiovascular disease (CVD) in older adults typically occurs in a context of multimorbidity. Current clinical practice and research mainly target single disease-specific care that does not embrace the complexities imposed by concurrent conditions. In this paper, emerging concepts regarding CVD in combination with multimorbidity are reviewed, including recommendations for incorporating multimorbidity into clinical decision making, critical knowledge gaps, and research priorities to optimize care of complex older patients.
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Affiliation(s)
- Daniel E Forman
- Department of Medicine, Section of Geriatric Cardiology, Veterans Affairs Geriatric Research Education, and Clinical Center, University of Pittsburgh, Pittsburgh, Pennsylvania.
| | - Mathew S Maurer
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, New York
| | - Cynthia Boyd
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ralph Brindis
- Phillip R. Lee Institute for Health Policy Studies, University of California-San Francisco, San Francisco, California
| | - Marcel E Salive
- Division of Geriatrics and Clinical Gerontology, National Institute on Aging, Bethesda, Maryland
| | | | - Susan P Bell
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - David B Reuben
- Division of Geriatrics, David Geffen School of Medicine at University of California-Los Angeles, Los Angeles, California
| | - Susan Zieman
- Division of Geriatrics and Clinical Gerontology, National Institute on Aging, Bethesda, Maryland
| | - Michael W Rich
- Division of Cardiology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
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16
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Ghorbani M, Ghaem H, Rezaianzadeh A, Shayan Z, Zand F, Nikandish R. Predictive factors associated with mortality and discharge in intensive care units: a retrospective cohort study. Electron Physician 2018; 10:6540-6547. [PMID: 29765580 PMCID: PMC5942576 DOI: 10.19082/6540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 09/16/2017] [Indexed: 12/12/2022] Open
Abstract
Background and aim Accurate prediction of prognosis of patients admitted to intensive care units (ICUs) is very important for the clinical management of the patients. The present study aims to identify independent factors affecting death and discharge in ICUs using competing risk modeling. Methods This retrospective cohort study was conducted on enrolling 880 patients admitted to emergency ICU in Namazi hospital, Shiraz University of Medical Sciences, Shiraz, Iran during 2013-2015. The data was collected from patients' medical records using a researcher-made checklist by a trained nurse. Competing risk regression models were fitted for the factors affecting the occurrence of death and discharge in ICU. Data analysis was conducted using STATA 13 and R 3.3.3 software. Results Among these patients, 682 (77.5%) were discharged and 157 (17.8%) died in the ICU. The patients' mean ± SD age was 48.90±19.52 yr. Among the study patients, 45.57% were female and 54.43% were male. In the competing risk model, age (Sub-distribution Hazard Ratio (SHR)) =1.02, 95% CI: 1.007-1.032), maximum heart rate (SHR=1.009, 95% CI: 1.001-1.019), minimum sodium level (SHR=1.035, 95% CI: 1.007-1.064), PH (SHR=7.982, 95% CI: 1.259-50.61), and bilirubin (SHR=1.046, 95% CI: 1.015-1.078) increased the risk of death, while maximum sodium level (SHR=0.946, 95% CI: 0.908-0.986) and maximum HCT (SHR=0.938, 95% CI: 0.882-0.998) reduced the risk of death. Conclusion In conclusion, the results of this study revealed several variables that were effective in ICU length of stay (LOS). The variables that independently influenced time-to-discharge were age, maximum systolic blood pressure, minimum HCT, maximum WBC, and urine output, maximum HCT and Glasgow coma score. The results also showed that age, maximum heart rate, maximum sodium level, PH, urine output, and bilirubin, minimum sodium level and maximum HCT were the predictors of death. Furthermore, our findings indicated that the competing risk model was more appropriate than the Cox model in evaluating the predictive factors associated with the occurrence of death and discharge in patients hospitalized in ICUs. Hence, this model could play an important role in managers' and clinicians' decision-making and improvement of the standard of care in ICUs.
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Affiliation(s)
- Mohammad Ghorbani
- Ph.D. of Epidemiology, Assistant Professor, Department of Public Health, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran
| | - Haleh Ghaem
- Ph.D. of Epidemiology, Assistant Professor, Research Center for Health Sciences, Institute of Health, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Abbas Rezaianzadeh
- MD, MPH, Ph.D. of Epidemiology, Professor, Colorectal Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Zahra Shayan
- Ph.D. of Biostatistics, Assistant Professor, Trauma Research Center, Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farid Zand
- M.D., Professor of Anesthesia and Critical Care Medicine, Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Nikandish
- M.D., Associate Professor of Anesthesia and Critical Care, Department of Emergency Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
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