15 research outputs found

    Definition of patient complexity in adults: A narrative review.

    Get PDF
    Background: Better identification of complex patients could help to improve their care. However, the definition of patient complexity itself is far from obvious. We conducted a narrative review to identify, describe, and synthesize the definitions of patient complexity used in the last 25 years. Methods: We searched PubMed for articles published in English between January 1995 and September 2020, defining patient complexity. We extended the search to the references of the included articles. We assessed the domains presented in the definitions, and classified the definitions as based on (1) medical aspects (e.g., number of conditions) or (2) medical and/or non-medical aspects (e.g., socio-economic status). We assessed whether the definition was based on a tool (e.g., index) or conceptual model. Results: Among 83 articles, there was marked heterogeneity in the patient complexity definitions. Domains contributing to complexity included health, demographics, behavior, socio-economic factors, healthcare system, medical decisionmaking, and environment. Patient complexity was defined according to medical aspects in 30 (36.1%) articles, and to medical and/or non-medical aspects in 53 (63.9%) articles. A tool was used in 36 (43.4%) articles, and a conceptual model in seven (8.4%) articles. Conclusion: A consensus concerning the definition of patient complexity was lacking. Most definitions incorporated nonmedical factors in the definition, underlining the importance of accounting not only for medical but also for non-medical aspects, as well as for their interrelationship

    Factors associated with one-year mortality after hospital discharge: A multicenter prospective cohort study.

    Get PDF
    OBJECTIVES 1) To identify predictors of one-year mortality in hospitalized medical patients using factors available during their hospital stay. 2) To evaluate whether healthcare system use within 30 days of hospital discharge is associated with one-year mortality. STUDY DESIGN AND SETTING This prospective, observational study included adult patients from four mid-sized hospital general internal medicine units. During index hospitalization, we retrieved patient characteristics, including demographic and socioeconomic indicators, diagnoses, and early simplified HOSPITAL scores from electronic health records and patient interviews. Data on healthcare system use was collected using telephone interviews 30 days after discharge. Survival status at one year was collected by telephone and from health records. We used a univariable analysis including variables available from the hospitalization and 30-day post-discharge periods. We then performed multivariable analyses with one model using index hospitalization data and one using 30-day post-discharge data. RESULTS Of 934 patients, 123 (13.2%; 95% CI 11.0-15.4%) were readmitted or died within 30 days. Of 814 patients whose primary outcome was available, 108 died (13.3%) within one year. Using factors obtained during hospitalization, the early simplified HOSPITAL score (OR 1.50; 95% CI 1.31-1.71; P < 0.001) and not living at home (OR 4.0; 95% CI 1.8-8.3; P < 0.001) were predictors of one-year mortality. Using 30-day post-discharge predictors, hospital readmission was significantly associated with one-year mortality (OR 4.81; 95% CI 2.77-8.33; P < 0.001). SIGNIFICANCE Factors predicting one-year mortality were a high early simplified HOSPITAL score, not living at home, and a 30-day unplanned readmission

    Comparison of 6 Mortality Risk Scores for Prediction of 1-Year Mortality Risk in Older Adults With Multimorbidity.

    Get PDF
    Importance The most appropriate therapy for older adults with multimorbidity may depend on life expectancy (ie, mortality risk), and several scores have been developed to predict 1-year mortality risk. However, often, these mortality risk scores have not been externally validated in large sample sizes, and a head-to-head comparison in a prospective contemporary cohort is lacking. Objective To prospectively compare the performance of 6 scores in predicting the 1-year mortality risk in hospitalized older adults with multimorbidity. Design, Setting, and Participants This prognostic study analyzed data of participants in the OPERAM (Optimising Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older People) trial, which was conducted between December 1, 2016, and October 31, 2018, in surgical and nonsurgical departments of 4 university-based hospitals in Louvain, Belgium; Utrecht, the Netherlands; Cork, Republic of Ireland; and Bern, Switzerland. Eligible participants in the OPERAM trial had multimorbidity (≄3 coexisting chronic diseases), were aged 70 years or older, had polypharmacy (≄5 long-term medications), and were admitted to a participating ward. Data were analyzed from April 1 to September 30, 2020. Main Outcomes and Measures The outcome of interest was any-cause death occurring in the first year of inclusion in the OPERAM trial. Overall performance, discrimination, and calibration of the following 6 scores were assessed: Burden of Illness Score for Elderly Persons, CARING (Cancer, Admissions ≄2, Residence in a nursing home, Intensive care unit admit with multiorgan failure, ≄2 Noncancer hospice guidelines) Criteria, Charlson Comorbidity Index, GagnĂ© Index, Levine Index, and Walter Index. These scores were assessed using the following measures: Brier score (0 indicates perfect overall performance and 0.25 indicates a noninformative model); C-statistic and 95% CI; Hosmer-Lemeshow goodness-of-fit test and calibration plots; and sensitivity, specificity, and positive and negative predictive values. Results The 1879 patients in the study had a median (IQR) age of 79 (74-84) years and 835 were women (44.4%). The median (IQR) number of chronic diseases was 11 (8-16). Within 1 year, 375 participants (20.0%) died. Brier scores ranged from 0.16 (GagnĂ© Index) to 0.24 (Burden of Illness Score for Elderly Persons). C-statistic values ranged from 0.62 (95% CI, 0.59-0.65) for Charlson Comorbidity Index to 0.69 (95% CI, 0.66-0.72) for the Walter Index. Calibration was good for the GagnĂ© Index and moderate for other mortality risk scores. Conclusions and Relevance Results of this prognostic study suggest that all 6 of the 1-year mortality risk scores examined had moderate prognostic performance, discriminatory power, and calibration in a large cohort of hospitalized older adults with multimorbidity. Overall, none of these mortality risk scores outperformed the others, and thus none could be recommended for use in daily clinical practice

    Patient Complexity Characteristics in the Hospital Setting

    No full text
    OBJECTIVES: To identify the characteristics of patients, diagnoses, treatments, processes, and communication that account for patient complexity as described by general internists in a hospital setting, as well as the frequency of patient complexity in the hospital setting. STUDY DESIGN: Multicenter cross-sectional survey at the departments of medicine of 3 large hospitals in Switzerland between July 2015 and October 2015. METHODS: A total of 111 general internists from 3 hospitals returned the survey, yielding a response rate of 53%. The survey had 21 closed-ended questions about the influence on patient complexity of factors in 4 categories of characteristics: patients’ characteristics, comorbidities and diagnoses, therapy, and hospital structure and process. RESULTS: The proportion of patients estimated to be complex was 42%. Multi­morbidity was the characteristic most frequently considered to influence patient complexity (95%; n = 106), followed by multiple therapy changes (94%; n = 104), psychiatric diseases (91%; n = 101), alcohol or drug abuse 91%; n = 101), communication barriers (89%; n = 99), several prescriptions (89%; n = 99), patient aggressiveness (88%; n = 98), therapy compliance (88%; n = 97), communication among divisions within the same hospital (85%; n = 94), and care coordination among providers (85%;n = 92). CONCLUSIONS: Several factors were identified as playing a role in hospital patient complexity, including multimorbidity, multiple therapy changes, psychiatric diseases, alcohol or drug abuse, and communication barriers

    Impact of hyponatremia correction on the risk for 30-day readmission and death in patients with congestive heart failure

    Full text link
    OBJECTIVE: The study objective was to compare the 30-day readmission rate and mortality between patients with heart failure who have persistent hyponatremia during hospitalization and patients who have their admission hyponatremia corrected before discharge. METHODS: This large retrospective cohort study included all adult patients admitted with a diagnosis of congestive heart failure to a tertiary-care hospital between July 2003 and October 2009. We compared the readmission rate and mortality 30 days after discharge between patients with persistent hyponatremia (ie, low sodium level at both admission and discharge) and patients with hyponatremia correction during hospitalization. RESULTS: Among the 4295 eligible patients with hyponatremia at admission, 1799 (41.9%) did not have their sodium level corrected at discharge. Overall, 1269 patients (29.5%) had a 30-day unplanned readmission or died. In a multivariable logistic regression analysis, the absence of hyponatremia correction was associated with a 45% increase in the odds of having a 30-day unplanned readmission or death (odds ratio, 1.45; 95% confidence interval, 1.27-1.67). Among patients with persistent hyponatremia, those with more severe hyponatremia at discharge (<130 mm/L) had a higher odds (odds ratio, 1.68; 95% confidence interval, 1.32-2.14) of having a 30-day readmission or death than those with less severe hyponatremia at discharge (130-134 mm/L). CONCLUSIONS: The absence of correction of hyponatremia over the course of hospitalization was frequent and independently associated with an increase of approximately 50% in the odds of having a 30-day unplanned readmission or death. This association appeared to be independent of heart failure severity

    Predicting Potential Adverse Events During a Skilled Nursing Facility Stay: A Skilled Nursing Facility Prognosis Score.

    No full text
    OBJECTIVES To derive a risk prediction score for potential adverse outcomes in older adults transitioning to a skilled nursing facility (SNF) from the hospital. DESIGN Retrospective analysis. SETTING Medicare Current Beneficiary Survey (2003-11). PARTICIPANTS Previously community-dwelling Medicare beneficiaries who were hospitalized and discharged to SNF for postacute care (N=2,043). MEASUREMENTS Risk factors included demographic characteristics, comorbidities, health status, hospital length of stay, prior SNF stays, SNF size and ownership, treatments received, physical function, and active signs or symptoms at time of SNF admission. The primary outcome was a composite of undesirable outcomes from the patient perspective, including hospital readmission during the SNF stay, long SNF stay (≄100 days), and death during the SNF stay. RESULTS Of the 2,043 previously community-dwelling beneficiaries hospitalized and discharged to a SNF for post-acute care, 589 (28.8%) experienced one of the three outcomes, with readmission (19.4%) most common, followed by mortality (10.5%) and long SNF stay (3.5%). A risk score including 5 factors (Barthel Index, Charlson-Deyo comorbidity score, hospital length of stay, heart failure diagnosis, presence of an indwelling catheter) demonstrated very good discrimination (C-statistic=0.75), accuracy (Brier score=0.17), and calibration for observed and expected events. CONCLUSION Older adults frequently experience potentially adverse outcomes in transitions to a SNF from the hospital; this novel score could be used to better match resources with patient risk

    Development and validation of a score to assess complexity of general internal medicine patients at hospital discharge: a prospective cohort study

    Get PDF
    Objective We aimed to develop and validate a score to assess inpatient complexity and compare its performance with two currently used but not validated tools to estimate complexity (ie, Charlson Comorbidity Index (CCI), patient clinical complexity level (PCCL)).Methods Consecutive patients discharged from the department of medicine of a tertiary care hospital were prospectively included into a derivation cohort from 1 October 2016 to 16 February 2017 (n=1407), and a temporal validation cohort from 17 February 2017 to 31 March 2017 (n=482). The physician in charge assessed complexity. Potential predictors comprised 52 parameters from the electronic health record such as health factors and hospital care usage. We fit a logistic regression model with backward selection to develop a prediction model and derive a score. We assessed and compared performance of model and score in internal and external validation using measures of discrimination and calibration.Results Overall, 447 of 1407 patients (32%) in the derivation cohort, and 116 of 482 patients (24%) in the validation cohort were identified as complex. Eleven variables independently associated with complexity were included in the score. Using a cut-off of ≄24 score points to define high-risk patients, specificity was 81% and sensitivity 57% in the validation cohort. The score’s area under the receiver operating characteristic (AUROC) curve was 0.78 in both the derivation and validation cohort. In comparison, the CCI had an AUROC between 0.58 and 0.61, and the PCCL between 0.64 and 0.69, respectively.Conclusions We derived and internally and externally validated a score that reflects patient complexity in the hospital setting, performed better than other tools and could help monitoring complex patients
    corecore