•Coronavirus disease 2019 (COVID-19) causes long-term symptoms including fatigue, depression, sleep disturbances, and muscle weakness.•COVID-19 causes altered metabolism of tryptophan in favor of the ...kynurenine pathway and disturbed tryptophan absorption due to altered expression of intestinal angiotensin-converting enzyme 2.•Both an increased kynurenine pathway and decreased absorption of tryptophan could be main contributors to long-term symptoms of COVID-19.
The global pandemic of COVID-19 has been lasting for more than one year and there is little known about the long-term health effects of the disease. Long-COVID is a new term that is used to describe the enduring symptoms of COVID-19 survivors. Huang et al. reported that fatigue, muscle weakness, sleep disturbances, anxiety, and depression were the most common complaints in COVID-19 survivors after 6 months of the infection. A recent meta-analysis showed that 80% of COVID-19 survivors have developed at least one long-term symptom and the most common five were fatigue, headache, attention deficit disorder, hair loss, and dyspnea. In this paper, we discuss the hypothesis that altered tryptophan absorption and metabolism could be the main contributor to the long-term symptoms in COVID-19 survivors.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Summary
Objective
This study aimed to identify predictors of internal medicine patients' readmission to hospital, using Andersen's behavioral model.
Methods
This prospective cohort study included ...2622 patients aged ≥18 years, who were admitted to internal medicine wards at a university hospital between 1 February 2015 and 31 January 2016. Independent variables were divided into four groups (predisposing, enabling, need, and utilization), based on Andersen's model, and included in stepwise logistic regression analysis.
Results
Younger age, male sex, a main diagnosis of neoplasm, longer length of stay, higher comorbidity scores, and weaker coping ability predicted all readmission. Predictors of unplanned readmission included having someone to help at home following discharge, comorbidity scores, and length of stay. Predictors of unplanned, related, and preventable readmissions included having someone to help at home following discharge, having a regular physician, and the main diagnosis at discharge. The most powerful predictors influencing readmission were need‐related variables.
Conclusion
Although some predictors of readmission were unalterable, they could be used to identify high‐risk patients. Innovative approaches targeting discharge planning and postdischarge care for patients with high comorbidity scores and long length of stay could reduce internal medicine patients' unplanned readmission.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
People living with HIV (PLWH) who have a normal CD4 T-cell count and suppressed viral load may not be at an increased risk for COVID-19. Nonetheless, the majority of PLWH have other chronic or acute ...multimorbidity, which increases their risk. Indeed, almost half of the PLWH in Europe are older than 50 years and have comorbidities. Drug–drug interactions are also prevalent in these individuals, and people with drug interactions have an increased risk of morbidity and mortality.1 It is highly recommended that treatment for COVID-19 in PLWH generally follow the same protocols as for patients without HIV.2
Aim
To determine the variables that affect patients’ perceptions about their readiness for discharge and to elucidate the effects of these perceptions on patient outcomes such as unplanned ...readmission to the hospital, emergency department visits and death within 30 days after discharge.
Background
In recent years, it has become even more important to assess patients’ readiness for discharge as patients tend to be discharged more quickly.
Methods
For the determination of patients’ self‐assessment, the Readiness for Hospital Discharge Scale/Short Form was utilized. This 1‐year prospective cohort study included 1,601 patients. Data were analysed using a chi‐square test, Mann–Whitney U test, univariate logistic regression analysis and multiple logistic regression analysis.
Results
The results of multiple logistic regression analysis revealed that age, sex, marital status, educational status, presence of someone to help at home after discharge and length of stay were predictors of patients’ readiness for hospital discharge. Furthermore, being unready for discharge increased the risk of 30 day unplanned readmission and 30 day death.
Conclusions
Considering these predictors, patients’ perceptions of readiness for discharge must be assessed before deciding to discharge them.
Implications for Nursing Management
Nurse managers can consider the predictors of patients’ readiness for discharge, thus the risk of unplanned readmission and death may be reduced.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK, VSZLJ
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China, on Jan 7, 2020. Over the following months, the virus rapidly spread throughout the world. ...Coronavirus Disease 2019 (COVID-19) can involve the gastrointestinal tract, including symptoms like nausea, vomiting and diarrhea and shedding of the SARS-CoV-2 in feces. Angiotensin-converting enzyme 2 (ACE2) protein, which has been proven to be a cell receptor for SARS-CoV-2, is expressed in the glandular cells of gastric, duodenal, and rectal epithelia, supporting the entry of SARS-CoV-2 into the host cells. According to the literature, rates of COVID-19 patients reporting diarrhea were between 7 - 14%. Diarrhea in the course of COVID-19 disease can cause dehydration and hospitalization. Although no antiviral drug was specifically designed for the treatment of diarrhea, several molecules could have beneficial effects by reducing viral replication. In this letter, we discussed the Levamisole, which is an anthelmintic agent with immunomodulatory effects, could be used effectively both for antiviral therapy and especially in COVID-19 patients with diarrhea.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Background
Internists who have an important role in the global response to the COVID‐19 pandemic are under both physical and psychological pressures.
Aims
To assess the anxiety among physicians ...working in the internal medicine department of a tertiary care hospital who are on the frontline of the COVID‐19 pandemic.
Methods
This single‐centre, non‐intervention, cross‐sectional descriptive study was conducted using an online survey questionnaire from 1 April to 14 April 2020. Physicians of the Department of Internal Medicine were invited to participate with a self‐administered questionnaire. The degree of symptoms of anxiety was assessed by the Turkish versions of the 7‐item Generalised Anxiety Disorder scale and Beck Anxiety Inventory, respectively.
Results
A total of 113 participants consented for the study and completed the questionnaire. The median age was 29 (IQR = 5) years and 53.1% were male. A total of 72 internists (63.7%) worked as ‘frontline’ healthcare workers directly engaged in diagnosing, treating or caring for patients with or suspected to have COVID‐19. Female gender was significantly associated with high scores and levels in all scales compared to the male gender (P < 0.005). Having family members over 65 years old and with chronic diseases were significantly associated with high anxiety scores and levels (P < 0.005).
Conclusions
In this survey of internists in a university hospital equipped with clinics, wards and intensive care unit for patients with COVID‐19, female gender and having family members over 65 years old and with chronic diseases were associated with increased anxiety levels.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•OXA-48(+) Klebsiella pneumoniae bloodstream infections have a high mortality rate.•Appropriate therapies conferred lower risk of mortality and greater chance of cure.•Thirty-day mortality rates were ...similar in the monotherapy and combination therapy arms.•Colistin resistance among these isolates had no significant impact on mortality.•All non-metallo-beta-lactamase producers remained susceptible to ceftazidime-avibactam.
This study aimed to characterize the epidemiology and clinical outcomes of patients with bloodstream infections (BSIs) due to carbapenem-resistant Klebsiella pneumoniae (CRKP) in an OXA-48-predominant environment. This was a retrospective single-centre cohort study including all consecutive patients with CRKP BSIs treated between 1 January 2014 and 31 December 2018. Multivariate analysis, subgroup analysis and propensity-score-matched analysis were employed to analyse 30-day mortality as the primary outcome. Clinical cure at day 14 was also analysed for the whole cohort. In total, 124 patients with unique isolates met all the inclusion criteria. OXA-48 was the most common type of carbapenemase (85.5%). Inappropriate therapy was significantly associated with 30-day mortality 70.6% vs 39.7%, adjusted odds ratio (aOR) 4.65, 95% confidence interval (CI) 1.50–14.40, P=0.008 and 14-day clinical failure (78.5% vs 56.2%, aOR 3.14, 95% CI 1.09–9.02, P=0.033) in multivariate analyses. Among those treated appropriately, the 30-day mortality rates were similar in monotherapy and combination therapy arms (OR 2.85, 95% CI 0.68–11.95, P=0.15). INCREMENT CPE mortality score (aOR 1.16, 95% CI 1.01–1.33, P=0.029), sepsis at BSI onset (aOR 2.90, 95% CI 1.02–8.27, P=0.046), and inappropriate therapy (aOR 4.65, 95% CI 1.50–14.40, P=0.008) were identified as independent risk factors for 30-day mortality. Colistin resistance in CRKP had no significant impact on 30-day mortality. These results were also confirmed in all propensity-score-matched analyses and sensitivity analyses. Appropriate regimens were associated with better clinical outcomes than inappropriate therapies for BSIs with CRKP predominantly possessing OXA-48.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Aim
The aim of this study is to test the validity and reliability of the Turkish version of the readiness for hospital discharge scale/short form.
Background
Assessment of readiness for discharge ...from the patient's perspective is becoming increasingly important for patient safety, satisfaction and various patient outcomes such as readmission, health service utilization and mortality. The readiness for hospital discharge scale/short form allows health care providers to determine patients' discharge readiness.
Methods
Participants were 1,579 inpatients from internal medicine departments. The readiness for hospital discharge scale/short form was translated into Turkish via back‐translation. We analyzed its reliability and validity via item analyses, an expert panel (content validity) and exploratory and confirmatory factor analyses (construct validity).
Results
The Cronbach's alpha of the whole scale was .74 and those for the subscales ranged from .79 to .93. The Spearman‐Brown reliability coefficient was .92. The confirmatory factor analysis revealed good fit indices (χ2/df = 2.6; RMSEA = .03; CFI = 1; GFI and AGFI = .99). The mean total score was 7.27 ± 1.85, while the subscale means ranged from 6.62 ± 3.41 to 7.69 ± 2.24.
Conclusion
The Turkish version of the readiness for hospital discharge scale/short form is a valid and reliable tool for assessing discharge readiness. The subscales with low means suggest opportunities for improvement.
Implications for nursing management
If readiness for hospital discharge scale/short form is valid and reliable, patients who are unready for discharge can be determined with this scale. Thus, nurse managers can determine what kind of measures should be taken for patients who are not ready for discharge, can control nursing practices related to these patients and can provide cooperation between the nurses and other health professionals.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK, VSZLJ
One of the most helpful strategies to deal with ongoing coronavirus pandemics is to use some prudence when treating patients infected with SARS-CoV-2. We aimed to evaluate the clinical, demographic, ...and laboratory parameters that might have predictive value for in-hospital mortality and the need for intensive care and build a model based on them. This study was a prospective, observational, single-center study including non-critical patients admitted to COVID-19 wards. Besides classical clinic-demographic features, basic laboratory parameters obtained on admission were tested, and then new models for each outcome were developed built on the most significant variables. Receiver operating characteristics (ROC) analyses were performed by calculating each model’s probability. A total of 368 non-critical hospitalized patients were recruited, the need for ICU care was observed in 70 patients (19%). The total number of patients who died in either ICU or wards was 39 (10.6%). The first two models (based on clinical features and demographics) were developed to predict ICU and death, respectively; older age, male sex, active cancer, and low baseline saturation were noted to be independent predictors. The area under the curve values of the first two models were noted 0.878 and 0.882 (
p
< .001; confidence interval CI 95% 0.837–0.919,
p
< .001; CI 95% 0.844–0.922). Following two models, the third and fourth were based on laboratory parameters with clinic-demographic features. Initial lower sodium and lower albumin levels were determined as independent factors in predicting the need for ICU care; higher blood urea nitrogen and lower albumin were independent factors in predicting in-hospital mortality. The area under the curve values of the third and fourth model was noted 0.938 and 0.929, respectively (
p
< .001; CI 95% 0.912–0.965,
p
< .001; CI 95% 0.895–962). By integrating the widely available blood tests results with simple clinic demographic data, non-critical patients can be stratified according to their risk level. Such stratification is essential to filter the patients’ non-critical underlying diseases and conditions that can obfuscate the physician’s predictive capacity.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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