scholarly journals Predicting Length of Stay and Discharge Destination for Surgical Patients: A Cohort Study

2020 ◽  
Vol 17 (24) ◽  
pp. 9490
Author(s):  
Fabrizio Bert ◽  
Omar Kakaa ◽  
Alessio Corradi ◽  
Annamaria Mascaro ◽  
Stefano Roggero ◽  
...  
Keyword(s):  
Cohort Study ◽  
Length Of Stay ◽  
Regression Models ◽  
Discharge Planning ◽  
Surgical Site Infections ◽  
Social Risk ◽  
Discharge Destination ◽  
Social Risk Factors ◽  
Logistic Regression Models ◽  
Expected Length

Discharge planning is important to prevent surgical site infections, reduce costs, and improve the hospitalization experience. The identification of early variables that can predict a longer-than-expected length of stay or the need for a discharge with additional needs can improve this process. A cohort study was conducted in the largest hospital of Northern Italy, collecting discharge records from January 2017 to January 2020 and pre-admission visits in the last three months. Socio-demographic and clinical data were collected. Linear and logistic regression models were fitted. The main outcomes were the length of stay (LOS) and discharge destination. The main predictors of a longer LOS were the need for additional care at discharge (+10.76 days), hospitalization from the emergency department (ED) (+5.21 days), and age (+0.04 days per year), accounting for clinical variables (p < 0.001 for all variables). Each year of age and hospitalization from the ED were associated with a higher probability of needing additional care at discharge (OR 1.02 and 1.77, respectively, p < 0.001). No additional findings came from pre-admission forms. Discharge difficulties seem to be related mainly to age and hospitalization procedures: those factors are probably masking underlying social risk factors that do not show up in patients with planned admissions.

scholarly journals Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study

2020 ◽  
Vol 7 (10) ◽  
Author(s):  
◽  
S K Mallipattu ◽  
R Jawa ◽  
R Moffitt ◽  
J Hajagos ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Cohort Study ◽  
Length Of Stay ◽  
Regression Models ◽  
Kidney Injury ◽  
Hospital Length ◽  
Hospitalized Patients ◽  
Hospital Length Of Stay ◽  
Logistic Regression Models

Abstract Background The global coronavirus disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals manage the care of hospitalized patients with varying demographics and clinical presentations. The goal of this study was to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. Methods This was a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020, to May 11, 2020. Geospatial distribution of study patients’ residences relative to population density in the region were mapped, and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. Results The median age of the study cohort (interquartile range [IQR]) was 62 (49–75) years, and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (rs = 0.235; P = .004), with noted “hot spots” in the region. Study patients were predominantly hypertensive (MAP &gt; 90 mmHg; 670, 51%) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (estimated glomerular filtration rate &lt; 60 mL/min/1.73 m2; 381, 29%). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, the median (IQR) hospital length of stay in survivors (22 [16.5–29.5] days) was significantly longer than that of nonsurvivors (15 [10–23.75] days), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15%, and in patients receiving IMV it was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project disposition in the remaining patients on ventilators. Acute kidney injury during hospitalization (odds ratioE, 3.23) was the strongest predictor of mortality in patients requiring IMV. Conclusions This is the first study to collectively utilize the demographics, clinical characteristics, and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.

scholarly journals Risk factors for mortality, ICU admission and bacteremia in patients suspected of sepsis at the Emergency department. A prospective cohort study

2020 ◽  
Author(s):  
Valentino D’Onofrio ◽  
Agnes Meersman ◽  
Sara Vijgen ◽  
Reinoud Cartuyvels ◽  
Peter Messiaen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Regression Models ◽  
Serum Lactate ◽  
Suspected Sepsis ◽  
Logistic Regression Models ◽  
Icu Admission ◽  
Observational Cohort ◽  
Independent Risk Factors ◽  
Prediction Of Prognosis

Abstract Background There is a clear need for a better assessment of independent risk factors for in-hospital mortality, ICU admission, and bacteremia in patients presenting with suspected sepsis at the ED. Methods A prospective observational cohort study including 1690 patients was performed. Two multivariable logistic regression models were used to identify independent risk factors. Results SOFA score of ≥2 and serum lactate of ≥2mmol/L were associated with all outcomes. Other independent risk factors were individual SOFA variables and SIRS variables but varied per outcome. MAP&lt;70 mmHg negatively impacted all outcomes. Conclusion These readily available measurements can help with early risk stratification and prediction of prognosis.

scholarly journals Work-life balance predicted work ability two years later: a cohort study of employees in the Swedish energy and water sector

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Erik Berglund ◽  
Ingrid Anderzén ◽  
Åsa Andersén ◽  
Per Lindberg
Keyword(s):  
Logistic Regression ◽  
Cohort Study ◽  
Work Ability ◽  
Regression Models ◽  
Work Life ◽  
Personal Life ◽  
Life Balance ◽  
Logistic Regression Models ◽  
Mental Demands ◽  
Longitudinal Associations

Abstract Background Work-life balance (WLB) is the extent to which individual’s multiple life roles and demands carry over between each role. WLB can be divided into work interference with personal life (WIPL) and personal life interference with work (PLIW). This study aimed to investigate longitudinal associations between WIPL, PLIW and work ability outcomes. Methods In this cohort study, 224 employees in the energy and water sector in Sweden were followed-up over 2 years. Three questions derived from the Work Ability Index were used for measuring work ability outcome: current work ability compared with lifetime best; work ability regarding physical; and mental demands. Logistic regression models were used to analyse longitudinal associations between work ability and WIPL and WIPL respectively, controlling for workplace (company), position at work, experience of leadership quality, demographics, and work ability. Results Work ability compared to lifetime best were associated with WIPL in the adjusted logistic regression models (odds ratio (OR) 1.77, 95% confidence interval (CI) 1.15–2.73), and PLIW (OR 3.34, 95% CI 1.66–6.74). Work ability regarding physical demands was associated with WIPL (OR 1.60, 95% CI 1.07–2.40). Work ability regarding mental demands was associated with WIPL (OR 1.59, 95% CI 1.03–2.44) and PLIW (OR 2.88, 95% CI 1.31–6.32). Conclusion In this two-year longitudinal study, lower WIPL predicted good/excellent overall work ability compared with lifetime best, higher work ability regarding physical and mental demands, and lower PLIW predicted good/excellent overall work ability compared with lifetime best and higher work ability regarding and mental demands.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

Correction of Hallux Valgus Deformity in Association With Underlying Metatarsus Adductus Deformity

2017 ◽  
Vol 10 (6) ◽  
pp. 538-542 ◽  
Author(s):  
Naohiro Shibuya ◽  
Daniel C. Jupiter ◽  
Britton S. Plemmons ◽  
Lanster Martin ◽  
Jakob C. Thorud
Keyword(s):  
Cohort Study ◽  
Hallux Valgus ◽  
Retrospective Cohort ◽  
Regression Models ◽  
Common Factors ◽  
Valgus Deformity ◽  
Corrective Surgery ◽  
Logistic Regression Models ◽  
Hallux Valgus Deformity ◽  
Metatarsus Adductus

Presence of underlying metatarsus deformity is known to negatively affect the outcome of hallux valgus corrective surgery. Yet there are many other factors that also affect final outcomes. In order to address the potential relationships between variables, we attempted to evaluate the association of metatarsus adductus with surgical outcomes, independent of other common factors used in evaluation of hallux valgus deformity. We enrolled 154 patients who had undergone hallux valgus corrective surgery and recorded their pre- and postoperative intermetatarsal (IM12) and hallux valgus (HVA) angles, preoperative Engel’s angle, and type of hallux valgus procedure. Using linear and logistic regression models, we identified variables that were independently (1) associated with the final IM12 and HVA and those (2) independently associated with dichotomized postoperative IM12 and HVA. We found that the only factors that were independently associated were preoperative IM12 with postoperative IM12, and preoperative HVA with postoperative HVA. In the linear model, a discretized Engel’s angle of 21 to 25 was marginally negatively associated with the final IM12. There was no detectable relationship between underlying metatarsus adductus deformity and the final outcome of hallux valgus correction after adjusting for other covariates. Levels of Evidence: Level III: Retrospective, cohort study

scholarly journals The significance of biological, environmental, and social risk factors for prostate cancer in a cohort study in Brazil

2012 ◽  
Vol 38 (6) ◽  
pp. 769-778 ◽  
Author(s):  
Frederico R. Romero ◽  
Antonio W. Romero ◽  
Rui Manuel S. de Almeida ◽  
Fernando Cesar de Oliveira Jr. ◽  
Renato Tambara Filho
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Cohort Study ◽  
Social Risk ◽  
Social Risk Factors

scholarly journals Associations Between Social Risk Factors and Surgical Site Infections After Colectomy and Abdominal Hysterectomy

2019 ◽  
Vol 2 (10) ◽  
pp. e1912339 ◽  
Author(s):  
Andrew C. Qi ◽  
Kate Peacock ◽  
Alina A. Luke ◽  
Abigail Barker ◽  
Margaret A. Olsen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Surgical Site Infections ◽  
Abdominal Hysterectomy ◽  
Social Risk ◽  
Social Risk Factors

scholarly journals Earlier onset of proteinuria or hypertension is a predictor of progression from gestational hypertension or gestational proteinuria to preeclampsia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mamoru Morikawa ◽  
Michinori Mayama ◽  
Kiwamu Noshiro ◽  
Yoshihiro Saito ◽  
Kinuko Nakagawa-Akabane ◽  
...  
Keyword(s):  
Cohort Study ◽  
Retrospective Cohort ◽  
Regression Models ◽  
Gestational Hypertension ◽  
P Value ◽  
Hypertensive Disorders Of Pregnancy ◽  
Multivariate Logistic Regression ◽  
Logistic Regression Models ◽  
Hypertensive Disorders ◽  
New Onset

AbstractAlthough gestational hypertension (GH) is a well-known disorder, gestational proteinuria (GP) has been far less emphasized. According to international criteria, hypertensive disorders of pregnancy include GH but not GP. Previous studies have not revealed the predictors of progression from GP to preeclampsia or those of progression from GH to preeclampsia. We aimed to determine both sets of predictors. A retrospective cohort study was conducted with singleton pregnant women who delivered at 22 gestational weeks or later. Preeclampsia was divided into three types: new onset of hypertension/proteinuria at 20 gestational weeks or later and additional new onset of other symptoms at < 7 days or at ≥ 7 days later. Of 94 women with preeclampsia, 20 exhibited proteinuria before preeclampsia, 14 experienced hypertension before preeclampsia, and 60 exhibited simultaneous new onset of both hypertension and proteinuria before preeclampsia; the outcomes of all types were similar. Of 34 women with presumptive GP, 58.8% developed preeclampsia; this proportion was significantly higher than that of 89 women with presumptive GH who developed preeclampsia (15.7%). According to multivariate logistic regression models, earlier onset of hypertension/proteinuria (before or at 34.7/33.9 gestational weeks) was a predicator for progression from presumptive GH/GP to preeclampsia (odds ratios: 1.21/1.21, P value: 0.0044/0.0477, respectively).

scholarly journals Biopsy in emergency gastroscopy does not increase the risk of rebleeding in patients with Forrest I acute nonvariceal upper gastrointestinal bleeding combined with suspected malignant gastric ulcer: a multicenter retrospective cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Quchuan Zhao ◽  
Tianyu Chi
Keyword(s):  
Logistic Regression ◽  
Gastric Ulcer ◽  
Cohort Study ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Retrospective Cohort ◽  
Regression Models ◽  
Upper Gastrointestinal Bleeding ◽  
Logistic Regression Models ◽  
Upper Gastrointestinal

Abstract Background Few studies have reported whether a biopsy in emergency gastroscopy (EG) increased the risk of rebleeding in patients with Forrest I acute nonvariceal upper gastrointestinal bleeding (ANVUGIB) combined with suspected malignant gastric ulcer (SMGU). This study aims to conduct a multicenter retrospective cohort study using propensity score matching to verify whether a biopsy in EG increases the risk of rebleeding in patients diagnosed with Forrest I ANVUGIB combined with SMGU. Methods Using the data for propensity-matched patients, logistic regression models were fitted using rebleeding as the dependent variable. Survival time was defined as the length of time the patient experienced from visiting the emergency department to rebleeding. We used the Kaplan–Meier (KM) method to analyze the 30-day survival of the patients with and without a biopsy after matching, and the log-rank test was performed to examine the differences in survival. Results With the use of propensity score matching, 308 patients who underwent a biopsy in EG were matched with 308 patients who did not. In the five logistic regression models, there were no significant group differences in the risk of rebleeding in patients with Forrest I ANVUGIB combined with SMGU between the biopsy and no-biopsy groups. The probability of survival was not significantly different between the no-biopsy and biopsy groups. Conclusions In this multicenter, retrospective propensity score matching cohort study, compared with patients without a biopsy, patients with a biopsy during EG had no increased risk of rebleeding, and there was no significant difference in the rate of rebleeding.

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