Development and Internal Validation of a Prediction Model for In-Hospital Mortality in Geriatric Patients With a Hip Fracture

Abstract Background People living with dementia seem to be more likely to experience delirium following hip fracture. The association between mental disorders (MD) and hip fracture remains controversial. We conducted a nationwide study to examine the prevalence of MD in geriatric patients with hip fractures undergoing surgery and conducted a related risk factor analysis. Material and methods This retrospective cohort study used data from Taiwan’s National Health Insurance Research Database between 2000 and 2012 and focused on people who were older than 60 years. Patients with hip fracture undergoing surgical intervention and without hip fracture were matched at a ratio of 1:1 for age, sex, comorbidities, and index year. The incidence and hazard ratios of age, sex, and multiple comorbidities related to MD and its subgroups were calculated using Cox proportional hazards regression models. Results A total of 1408 patients in the hip fracture group and a total of 1408 patients in the control group (no fracture) were included. The overall incidence of MD for the hip fracture and control groups per 100 person-years were 0.8 and 0.5, respectively. Among MD, the incidences of transient MD, depression, and dementia were significantly higher in the hip fracture group than in the control group. Conclusions The prevalence of newly developed MD, especially transient MD, depression, and dementia, was higher in the geriatric patients with hip fracture undergoing surgery than that in the control group. Prompt and aggressive prevention protocols and persistent follow-up of MD development is highly necessary in this aged society.

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Development and internal validation of the HIV In‐hospital Mortality Prediction (HIV‐IMP) risk score

HIV Medicine ◽

10.1111/hiv.13159 ◽

2021 ◽

Author(s):

Abdullah E. Laher ◽

Fathima Paruk ◽

Willem D. F. Venter ◽

Oluwatosin A. Ayeni ◽

Feroza Motara ◽

...

Keyword(s):

Hospital Mortality ◽

Risk Score ◽

Mortality Prediction ◽

Internal Validation

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Development and internal validation of a humeral torsion prediction model in professional baseball pitchers

Journal of Shoulder and Elbow Surgery ◽

10.1016/j.jse.2021.05.022 ◽

2021 ◽

Author(s):

Garrett S. Bullock ◽

Ellen Shanley ◽

Gary S. Collins ◽

Nigel K. Arden ◽

Thomas K. Noonan ◽

...

Keyword(s):

Prediction Model ◽

Professional Baseball ◽

Internal Validation ◽

Humeral Torsion ◽

Baseball Pitchers

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Diabetes Confers Little to No Increased Risk of Postoperative Complications After Hip Fracture Surgery in Geriatric Patients

Clinical Orthopaedics and Related Research ◽

10.1007/s11999-014-3945-7 ◽

2014 ◽

Vol 473 (3) ◽

pp. 1043-1051 ◽

Cited By ~ 19

Author(s):

Nicholas S. Golinvaux ◽

Daniel D. Bohl ◽

Bryce A. Basques ◽

Michael R. Baumgaertner ◽

Jonathan N. Grauer

Keyword(s):

Hip Fracture ◽

Postoperative Complications ◽

Geriatric Patients ◽

Hip Fracture Surgery ◽

Increased Risk ◽

Fracture Surgery

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Development and internal validation of a clinical prediction model of the risk of nosocomial bacterial infection in patients with systemic lupus erythematosus

Revista Colombiana de Reumatología (English Edition) ◽

10.1016/j.rcreue.2020.07.003 ◽

2021 ◽

Author(s):

Mauricio Restrepo-Escobar ◽

Pablo Castaño-González ◽

Marianella Galvis-García ◽

Laura Morales-Maya ◽

Tomás Urrego ◽

...

Keyword(s):

Systemic Lupus Erythematosus ◽

Prediction Model ◽

Bacterial Infection ◽

Lupus Erythematosus ◽

Clinical Prediction ◽

Systemic Lupus ◽

Internal Validation ◽

Clinical Prediction Model

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Development and Validation of a Risk Prediction Model for Patients with COVID-19 Using Linked Population Health Data (Preprint)

10.2196/preprints.35617 ◽

2021 ◽

Author(s):

Nikolaos Mastellos ◽

Richard Betteridge ◽

Prasanth Peddaayyavarla ◽

Andrew Moran ◽

Jurgita Kaubryte ◽

...

Keyword(s):

Health Care ◽

Prediction Model ◽

Population Health ◽

Risk Prediction ◽

Validation Cohort ◽

Risk Prediction Model ◽

Adult Patients ◽

Internal Validation ◽

Temporal Validation ◽

Development And Validation

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

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Efficacy of a multidisciplinary care protocol for the treatment of operated hip fracture patients

Scientific Reports ◽

10.1038/s41598-021-03415-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jorge Salvador-Marín ◽

Francisco Javier Ferrández-Martínez ◽

Cort D. Lawton ◽

Domingo Orozco-Beltrán ◽

Jose Fernando Martínez-López ◽

...

Keyword(s):

Hip Fracture ◽

Hospital Mortality ◽

Multivariable Analysis ◽

Hospital Costs ◽

Length Of Hospital Stay ◽

Multidisciplinary Care ◽

Exposed Group ◽

Composite Outcome ◽

Surgical Delay ◽

Care Protocol

AbstractTo assess the effects of a multidisciplinary care protocol on cost, length of hospital stay (LOS), and mortality in hip-fracture-operated patients over 65 years. Prospective cohort study between 2011 and 2017. The unexposed group comprised patients who did not receive care according to the multidisciplinary protocol, while the exposed group did. Variables analyzed were demographics, medical comorbidities, treatment, blood parameters, surgical delay, LOS, re-admissions, mortality, and a composite outcome considering in-hospital mortality and/or LOS > 10 days. We performed a Poisson regression and cost analysis. The cohort included 681 patients: 310 unexposed and 371, exposed. The exposed group showed a shorter surgical delay (3.0 vs. 3.6 days; p < 0.001), and a higher proportion received surgery within 48 h (46.1% vs. 34.2%, p = 0.002). They also showed lower rates of 30-day readmission (9.4% vs. 15.8%, p = 0.012), 30-day mortality (4.9% vs. 9.4%, p = 0.021), in-hospital mortality (3.5% vs. 7.7%; p = 0.015), and LOS (8.4 vs. 9.1 days, p < 0.001). Multivariable analysis showed a protective effect of the protocol on the composite outcome (risk ratio 0.62, 95% CI 0.48–0.80, p < 0.001). Hospital costs were reduced by EUR 112,153.3. A multidisciplinary shared care protocol was associated with a reduction in the LOS, surgical delay, 30-day readmissions, and in-hospital and 30-day mortality, in hip-fracture-operated patients.

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