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Background: The simplified HOSPITAL score is an easy-to-use prediction model to identify patients at high risk of 30-day readmission before hospital discharge. An earlier stratification of this risk would allow more preparation time for transitional care interventions., Objective: To assess whether the simplified HOSPITAL score would perform similarly by using hemoglobin and sodium level at the time of admission instead of discharge., Design: Prospective national multicentric cohort study., Participants: In total, 934 consecutively discharged medical inpatients from internal general services., Main Measures: We measured the composite of the first unplanned readmission or death within 30 days after discharge of index admission and compared the performance of the simplified score with lab at discharge (simplified HOSPITAL score) and lab at admission (early HOSPITAL score) according to their discriminatory power (Area Under the Receiver Operating characteristic Curve (AUROC)) and the Net Reclassification Improvement (NRI)., Key Results: During the study period, a total of 3239 patients were screened and 934 included. In total, 122 (13.2%) of them had a 30-day unplanned readmission or death. The simplified and the early versions of the HOSPITAL score both showed very good accuracy (Brier score 0.11, 95%CI 0.10-0.13). Their AUROC were 0.66 (95%CI 0.60-0.71), and 0.66 (95%CI 0.61-0.71), respectively, without a statistical difference (p value 0.79). Compared with the model at discharge, the model with lab at admission showed improvement in classification based on the continuous NRI (0.28; 95%CI 0.08 to 0.48; p value 0.004)., Conclusion: The early HOSPITAL score performs, at least similarly, in identifying patients at high risk for 30-day unplanned readmission and allows a readmission risk stratification early during the hospital stay. Therefore, this new version offers a timely preparation of transition care interventions to the patients who may benefit the most., (© 2023. The Author(s).)

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: JDL is supported by grants from the Swiss National Science Foundation (SNF 160072 and 185592) as well as by Swiss Personalised Health Network (SPHN 2018DR108). JDL has also re-ceived unrestricted grants from AstraZeneca AG Switzerland, Boehringer Ingelheim GmbH Switzerland, GSK AG Switzerland and Novartis AG Switzerland. JDD is supported by Swiss National Science Foundation (grant number 170656). This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Liechti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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, Gagné 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 (Gagné 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 Gagné 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.

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 decision-making, 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 non-medical 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., Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.)

Objectives: Readmission and death are frequent after a hospitalisation and difficult to predict. While many predictors have been identified, few studies have focused on functional status. We assessed whether performance-based functional impairment at discharge is associated with readmission and death after an acute medical hospitalisation., Design, Setting and Participants: We prospectively included patients aged ≥50 years admitted to the Department of General Internal Medicine of a large community hospital. Functional status was assessed shortly before discharge using the Timed Up and Go test performed twice in a standard way by trained physiotherapists and was defined as a test duration ≥15 s. Sensitivity analyses using a cut-off at >10 and >20 s were performed., Primary and Secondary Outcome Measures: The primary and secondary outcome measures were unplanned readmission and death, respectively, within 6 months after discharge., Results: Within 6 months after discharge, 107/338 (31.7%) patients had an unplanned readmission and 31/338 (9.2%) died. Functional impairment was associated with higher risk of death (OR 2.44, 95% CI 1.15 to 5.18), but not with unplanned readmission (OR 1.34, 95% CI 0.84 to 2.15). No significant association was found between functional impairment and the total number of unplanned readmissions (adjusted OR 1.59, 95% CI 0.95 to 2.67)., Conclusions: Functional impairment at discharge of an acute medical hospitalisation was associated with higher risk of death, but not of unplanned readmission within 6 months after discharge. Simple performance-based assessment may represent a better prognostic measure for mortality than for readmission., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Background/objectives: New tools to accurately identify potentially preventable 30-day readmissions are needed. The HOSPITAL score has been internationally validated for medical inpatients, but its performance in select conditions targeted by the Hospital Readmission Reduction Program (HRRP) is unknown., Design: Retrospective cohort study., Setting: Six geographically diverse medical centers., Participants/exposures: All consecutive adult medical patients discharged alive in 2011 with 1 of the 4 medical conditions targeted by the HRRP (acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and heart failure) were included. Potentially preventable 30-day readmissions were identified using the SQLape algorithm. The HOSPITAL score was calculated for all patients., Measurements: A multivariable logistic regression model accounting for hospital effects was used to evaluate the accuracy (Brier score), discrimination (c-statistic), and calibration (Pearson goodness-of-fit) of the HOSPITAL score for each 4 medical conditions., Results: Among the 9181 patients included, the overall 30-day potentially preventable readmission rate was 13.6%. Across all 4 diagnoses, the HOSPITAL score had very good accuracy (Brier score of 0.11), good discrimination (c-statistic of 0.68), and excellent calibration (Hosmer-Lemeshow goodness-of-fit test, P=0.77). Within each diagnosis, performance was similar. In sensitivity analyses, performance was similar for all readmissions (not just potentially preventable) and when restricted to patients age 65 and above., Conclusions: The HOSPITAL score identifies a high-risk cohort for potentially preventable readmissions in a variety of practice settings, including conditions targeted by the HRRP. It may be a valuable tool when included in interventions to reduce readmissions within or across these conditions., Competing Interests: No other authors have conflicts of interest to report.

Purpose: Patients with cancer are particularly at risk for readmission within 30-days after discharge. To identify the patients who might benefit from more-intensive discharge interventions, we identified the risk factors associated with 30-day potentially avoidable readmissions., Methods and Materials: We included all consecutive discharges from the oncology division of an academic tertiary medical center in Boston, Massachusetts, between July 1, 2009, and June 30, 2010. Potentially avoidable 30-day readmissions to the index hospital and two other hospitals within its network were identified. We performed a multivariable logistic regression in which the final model included variables found in bivariable testing to be significantly associated with the outcome., Results: Among the 2,916 patients discharged during the study period, 1,086 (37.3%) were readmitted within 30 days. Of these, 341 (31.4% of all readmissions, 11.7% of all discharges) were identified as potentially avoidable. In the multivariable analysis, the following patient factors were associated with a significantly higher risk of a potentially avoidable readmission: total number of medications at discharge, liver disease, last sodium level, and last hemoglobin level before discharge. In addition, potentially avoidable readmissions occurred significantly earlier than unavoidable readmissions (median, 10 v 13 days; P < .001)., Conclusion: Almost 40% of patients with cancer had a 30-day readmission, and almost one third of these were deemed potentially avoidable, and several risk factors for this were identified. Interventions at discharge may be prioritized to patients with these risk factors.

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., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Importance: Identification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit., Objective: To externally validate the HOSPITAL score in an international multicenter study to assess its generalizability., Design, Setting, and Participants: International retrospective cohort study of 117 065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded., Exposures: The HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay., Main Outcomes and Measures: 30-day potentially avoidable readmission to the index hospital using the SQLape algorithm., Results: Overall, 117 065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16 992 of 117 065 (14.5%) were followed by a 30-day readmission, and 11 307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n = 73 031 [62.4%]), intermediate (n = 27 612 [23.6%]), and high risk (n = 16 422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson χ2 test P = .89)., Conclusions and Relevance: The HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.

Objectives: To evaluate the impact of preoperative sepsis on risk of postoperative arterial and venous thromboses., Design: Prospective cohort study using the National Surgical Quality Improvement Program database of the American College of Surgeons (ACS-NSQIP)., Setting: Inpatient and outpatient procedures in 374 hospitals of all types across the United States, 2005-12., Participants: 2,305,380 adults who underwent surgical procedures., Main Outcome Measures: Arterial thrombosis (myocardial infarction or stroke) and venous thrombosis (deep venous thrombosis or pulmonary embolism) in the 30 days after surgery., Results: Among all surgical procedures, patients with preoperative systemic inflammatory response syndrome or any sepsis had three times the odds of having an arterial or venous postoperative thrombosis (odds ratio 3.1, 95% confidence interval 3.0 to 3.1). The adjusted odds ratios were 2.7 (2.5 to 2.8) for arterial thrombosis and 3.3 (3.2 to 3.4) for venous thrombosis. The adjusted odds ratios for thrombosis were 2.5 (2.4 to 2.6) in patients with systemic inflammatory response syndrome, 3.3 (3.1 to 3.4) in patients with sepsis, and 5.7 (5.4 to 6.1) in patients with severe sepsis, compared with patients without any systemic inflammation. In patients with preoperative sepsis, both emergency and elective surgical procedures had a twofold increased odds of thrombosis., Conclusions: Preoperative sepsis represents an important independent risk factor for both arterial and venous thromboses. The risk of thrombosis increases with the severity of the inflammatory response and is higher in both emergent and elective surgical procedures. Suspicion of thrombosis should be higher in patients with sepsis who undergo surgery., (© Donzé et al 2014.)