BACKGROUND: The primary aim of this study was to assess 30-day and one-year rates for unplanned readmission due to implant-associated complications following total hip (THA) or total knee arthroplasty (TKA) in Austria. Secondary endpoints were reasons for readmission and differences in revision risk depending on demographics and hospital size.
METHODS: Data on patients receiving THA (n = 18,508) or TKA (n = 15,884) in orthopaedic and trauma units across Austria within a one-year period (January 2021 to December 2021) was retrieved from a government-maintained database. The absolute and relative frequencies of unplanned readmissions were calculated. Risk factors for 30-day and one-year readmission following THA or TKA due to implant-associated complications were investigated.
RESULTS: The thirty-day and one-year readmission rates for any implant-associated complication were 1.0% (339 of 34,392) and 3.0% (1,024 of 34,392). Relative to the overall readmission rate for any complication at 30 days (n = 1,952) and one year (n = 12,109), respectively, readmission rates for implant-associated complications were 17.4 and 8.5%. The thirty-day readmission rates were higher in THA (1.2%) than TKA patients (0.8%; P = 0.001), while it was the opposite at one year (THA, 2.7%; TKA, 3.3%; P < 0.001). Mechanical complications (554 of 1,024) were the most common reason for one-year readmission. Prolonged length of in-hospital stay independently associated with increased one-year readmission risk in THA and TKA patients. Treatment at large-sized hospitals was associated with a higher one-year readmission risk in TKA patients.
CONCLUSIONS: The thirty-day and one-year readmission rates for implant-associated complications following THA or TKA in Austria are lower than reported in other countries, with similar risk factors and reasons for readmission. Considering that almost 20% of unplanned hospital readmissions following total joint arthroplasty are attributable to implant-associated complications, optimization of in-hospital and post-discharge medical care for these patients is warranted.

BACKGROUND: Little is known about the prevalence of malnutrition among patients receiving home care (HC) and ambulatory care (AC) services. Further, the risk of hospital readmission in malnourished patients transitioning from hospital to HC or AC is also not well established. This study aims to address these two gaps.
METHODS: A descriptive cohort study of newly referred HC and AC patients between January and December 2019 was conducted. Nutrition status was assessed by clinicians using the Mini Nutritional Assessment-Short Form (MNA-SF). Prevalence of malnutrition and at risk of malnutrition (ARM) was calculated, and a log-binomial regression model was used to estimate the relative risk of hospital readmission within 30 days of discharge for those who were malnourished and referred from hospital.
RESULTS: A total of 3704 MNA-SFs were returned, of which 2402 (65%) had complete data. The estimated prevalence of malnutrition and ARM among newly referred HC and AC patients was 21% (95% CI: 19%-22%) and 55% (95% CI: 53%-57%), respectively. The estimated risk of hospital readmission for malnourished patients was 2.7 times higher (95% CI: 1.9%-3.9%) and for ARM patients was 1.9 times higher (95% CI: 1.4%-2.8%) than that of patients with normal nutrition status.
CONCLUSIONS: The prevalence of malnutrition and ARM among HC and AC patients is high. Malnutrition and ARM are correlated with an increased risk of hospital readmission 30 days posthospital discharge.

UNASSIGNED: To determine timing and risk factors associated with readmission within 30 days of discharge following noncardiac surgery.
UNASSIGNED: Hospital readmission after noncardiac surgery is costly. Data on the drivers of readmission have largely been derived from single-center studies focused on a single surgical procedure with uncertainty regarding generalizability.
UNASSIGNED: We undertook an international (28 centers, 14 countries) prospective cohort study of a representative sample of adults ≥45 years of age who underwent noncardiac surgery. Risk factors for readmission were assessed using Cox regression (ClinicalTrials.gov, NCT00512109).
UNASSIGNED: Of 36,657 eligible participants, 2744 (7.5%; 95% confidence interval [CI], 7.2-7.8) were readmitted within 30 days of discharge. Rates of readmission were highest in the first 7 days after discharge and declined over the follow-up period. Multivariable analyses demonstrated that 9 baseline characteristics (eg, cancer treatment in past 6 months; adjusted hazard ratio [HR], 1.44; 95% CI, 1.30-1.59), 5 baseline laboratory and physical measures (eg, estimated glomerular filtration rate or on dialysis; HR, 1.47; 95% CI, 1.24-1.75), 7 surgery types (eg, general surgery; HR, 1.86; 95% CI, 1.61-2.16), 5 index hospitalization events (eg, stroke; HR, 2.21; 95% CI, 1.24-3.94), and 3 other factors (eg, discharge to nursing home; HR, 1.61; 95% CI, 1.33-1.95) were associated with readmission.
UNASSIGNED: Readmission following noncardiac surgery is common (1 in 13 patients). We identified perioperative risk factors associated with 30-day readmission that can help frontline clinicians identify which patients are at the highest risk of readmission and target them for preventive measures.

BACKGROUND: Hospital-acquired adverse drug reactions (HA-ADRs) are common in older adults. However, there is limited knowledge regarding the association between HA-ADRs and adverse clinical outcomes.
OBJECTIVE: To investigate the incidence and characteristics of HA-ADRs in older adults, and any association with mortality, length of stay, and readmissions.
METHODS: Prospective cohort study.
METHODS: Flinders Medical Centre, a large tertiary referral hospital in Adelaide, South Australia. Older adults admitted under the General Medicine and Acute Care of the Elderly units with no previous diagnosis of dementia.
METHODS: All patients had a Multidimensional Prognostic Index (MPI) assessment performed within 3 days of the admission. Data collected included age, gender, estimated glomerular filtration rate (eGFR), length of stay, readmissions, and mortality. HA-ADRs were identified by review of individual discharge summaries. Univariate and multivariate analyses were performed to investigate associations with clinical outcomes including mortality, length of stay, and readmissions. Exploratory analyses were performed for HA-ADR groups based on Medical Dictionary for Regulatory Activities System Organ Class and World Health Organization Anatomical Therapeutic Chemical classifications that accounted for ≥10% of all HA-ADRs.
RESULTS: There were 737 patients in the cohort with 72 having experienced a HA-ADRs (incidence = 9.8%). Patients with an HA-ADR had increased length of stay and 30-day readmissions compared with those without an HA-ADR. In multivariate analysis, the number of HA-ADRs was associated with in-hospital mortality and length of stay but not post-discharge mortality or readmissions within 30 days. In exploratory analyses, patients with an HA-ADR to antibacterial drugs had significantly higher rates of in-hospital mortality compared with those without these reactions.
CONCLUSIONS: The number of HA-ADRs are associated with in-hospital mortality and length of stay in older Australian inpatients. The occurrence of HA-ADRs may be a trigger to offer advice to prescribers to prevent future ADRs to similar agents and proactively manage disease to improve health outcomes.

OBJECTIVE: The aim of this study was to assess the risk factors associated with 30-day hospital readmissions after a cholecystectomy.
METHODS: We conducted a case-control study, with data obtained from UC-Christus from Santiago, Chile. All patients who underwent a cholecystectomy between January 2015 and December 2019 were included in the study. We identified all patients readmitted after a cholecystectomy and compared them with a randomized control group. Univariate and multivariate analyses were conducted to identify risk factors.
RESULTS: Of the 4866 cholecystectomies performed between 2015 and 2019, 79 patients presented 30-day hospital readmission after the surgical procedure (1.6%). We identified as risk factors for readmission in the univariate analysis the presence of a solid tumor at the moment of cholecystectomy (OR = 7.58), high pre-operative direct bilirubin (OR = 2.52), high pre-operative alkaline phosphatase (OR = 3.25), emergency admission (OR = 2.04), choledocholithiasis on admission (OR = 4.34), additional surgical procedure during the cholecystectomy (OR = 4.12), and post-operative complications. In the multivariate analysis, the performance of an additional surgical procedure during cholecystectomy was statistically significant (OR = 4.24).
CONCLUSIONS: Performing an additional surgical procedure during cholecystectomy was identified as a risk factor associated with 30-day hospital readmission.
OBJECTIVE: El objetivo de este estudio fue evaluar los factores de riesgo asociados al reingreso hospitalario en los primeros 30 días post colecistectomía.
UNASSIGNED: Estudio de casos-controles con datos obtenidos del Hospital Clínico de la UC-Christus, Santiago, Chile. Se ­incluyeron las colecistectomías realizadas entre los años 2015-2019. Se consideraron como casos aquellos pacientes que reingresaron en los 30 primeros días posterior a una colecistectomía. Se realizó un análisis univariado y multivariado de diferentes posibles factores de riesgo.
RESULTS: De un total de 4866 colecistectomías, 79 pacientes presentaron reingreso hospitalario. Los resultados estadísticamente significativos en el análisis univariado fueron; tumor sólido al momento de la colecistectomía (OR = 7.58) bilirrubina directa preoperatoria alterada (OR = 2.52), fosfatasa alcalina preoperatoria alterada (OR = 3.25), ingreso de urgencia (OR = 2.04), coledocolitiasis al ingreso (OR = 4.34) realización de otros procedimientos (OR = 4.12) y complicaciones postoperatorias. En el análisis multivariado sólo la realización de otro procedimiento durante la colecistectomía fue estadísticamente significativa (OR = 4.24).
UNASSIGNED: La realización de otros procedimientos durante la colecistectomía es un factor de riesgo de reingreso hospitalario en los 30 días posteriores a la colecistectomía.

BACKGROUND: As the first comprehensive investigation into hospital readmissions following robotic hepatectomy for neoplastic disease, this study aims to fill a critical knowledge gap by evaluating risk factors associated with readmission and their impact on survival and the financial burden.
METHODS: The study analyzed a database of robotic hepatectomy patients, comparing readmitted and non-readmitted individuals post-operatively using 1:1 propensity score matching. Statistical methods included Chi-square, Mann-Whitney U, T-test, binomial logistic regression, and Kaplan-Meier analysis.
RESULTS: Among 244 patients, 44 were readmitted within 90 days. Risk factors included hypertension (p ​= ​0.01), increased Child-Pugh score (p ​< ​0.01), and R1 margin status (p ​= ​0.05). Neoadjuvant chemotherapy correlated with lower readmission risk (p ​= ​0.045). Readmissions didn\'t significantly impact five-year survival (p ​= ​0.42) but increased fixed indirect hospital costs (p ​< ​0.01).
CONCLUSIONS: Readmission post-robotic hepatectomy correlates with hypertension, higher Child-Pugh scores, and R1 margins. The use of neoadjuvant chemotherapy was associated with a lower admission rate due to less diffuse liver disease in these patients. While not affecting survival, readmissions elevate healthcare costs.

The lack of a widely accepted, broadly validated tool for diagnosing malnutrition in hospitalized patients limits the ability to assess the integral role of nutrition as an input and outcome of health, disease, and treatment.
This study aimed to evaluate the predictive validity of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition (ASPEN) indicators to diagnose malnutrition (AAIM) tool and determine if it can be simplified.
A prospective cohort study was conducted from August 2019 to September 2022 with 32 hospitals in United States. At baseline, 290 adult patients were evaluated for a diagnosis of malnutrition using the AAIM tool, which assesses weight loss, inadequate energy intake, subcutaneous fat and muscle loss, edema, and hand grip strength. Healthcare outcomes were extracted from the medical record: composite incidence of emergency department (ED) visits and hospital readmissions within 90 d postdischarge; length of hospital stay (LOS); and Medicare Severity Disease Related Group (MS-DRG) relative weight (i.e., healthcare resource utilization). We used multilevel, multivariable negative binomial or generalized linear regression models to evaluate relationships between malnutrition diagnosis and healthcare outcomes.
After adjusting for disease severity and acuity and sociodemographic characteristics, individuals diagnosed with severe malnutrition had a higher incidence rate of ED visits and hospital readmissions (incidence rate ratio: 1.89; 95% CI: 1.14, 3.13; P = 0.01), and individuals diagnosed with moderate malnutrition had a 25.2% longer LOS (95% CI: 2.0%, 53.7%; P = 0.03) and 15.1% greater healthcare resource utilization (95% CI: 1.6%, 31.9%; P = 0.03) compared with individuals with no malnutrition diagnosis. Observed relationships remained consistent when only considering malnutrition diagnoses supported by at least 2 of these indicators: weight loss, subcutaneous fat loss, muscle wasting, and inadequate energy intake.
Findings from this multihospital study confirm the predictive validity of the original or simplified AAIM tool and support its routine use for hospitalized adult patients. This trial was registered at clinicaltrials.gov as NCT03928548 (https://classic.
gov/ct2/show/NCT03928548).

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.
METHODS: Prospective national multicentric cohort study.
METHODS: In total, 934 consecutively discharged medical inpatients from internal general services.
METHODS: 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).
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).
CONCLUSIONS: 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.

We aimed to explore managerial and project staff perceptions of the pilot implementation of an algorithm-supported care navigation model, targeting people at risk of hospital readmission. The pilot was implemented from May to November 2017 at a Victorian health service (Australia) and provided to sixty-five patients discharged from the hospital to the community. All managers and the single clinician involved participated in a semi-structured interview. Participants (n = 6) were asked about their perceptions of the service design and the enablers and barriers to implementation. Interviews were transcribed verbatim and analysed according to a framework approach, using inductive and deductive techniques. Constructed themes included the following: an algorithm alone is not enough, the health service culture, leadership, resources and the perceived patient experience. Participants felt that having an algorithm to target those considered most likely to benefit was helpful but not enough on its own without addressing other contextual factors, such as the health service\'s capacity to support a large-scale implementation. Deductively mapping themes to the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework highlighted that a formal facilitation would be essential for future sustainable implementations. The systematic identification of barriers and enablers elicited critical information for broader implementations of algorithm-supported models of care.

Transitional care programs (TCPs), where hospital care team members repeatedly follow up with discharged patients, aim to reduce post-discharge hospital or emergency department (ED) utilization and healthcare costs. We examined the effectiveness of TCPs at reducing healthcare costs, hospital readmissions, and ED visits. Centers for Medicare and Medicaid Services Bundled Payments for Care Improvement (BPCI) program adjudicated claims files and electronic health records from Greenville Memorial Hospital, Greenville, SC, were accessed. Data on post-discharge 30- and 90-day ED visits and readmissions, total costs, and episodes with costs over BPCI target prices were extracted from November 2017 to July 2020 and compared between the \"TCP-Graduates\" (N = 85) and \"Did Not Graduate\" (DNG) (N = 1310) groups. As compared to the DNG group, the TCP-Graduates group had significantly fewer 30-day (7.1% vs. 14.9%, p = 0.046) and 90-day (15.5% vs. 26.3%, p = 0.025) readmissions, episodes with total costs over target prices (25.9% vs. 36.6%, p = 0.031), and lower total cost/episode (USD 22,439 vs. USD 28,633, p = 0.018), but differences in 30-day (9.4% vs. 11.2%, p = 0.607) and 90-day (20.0% vs. 21.9%, p = 0.680) ED visits were not significant. TCP was associated with reduced post-discharge hospital readmissions, total care costs, and episodes exceeding target prices. Further studies with rigorous designs and individual-level data should test these findings.