Background
To investigate the temporal characteristics of clinical variables of hospital-acquired acute kidney injury (AKI) in COVID-19 patients and to longitudinally predict AKI onset.
Methods
There ...were 308 hospital-acquired AKI and 721 non-AKI (NAKI) COVID-19 patients from Stony Brook Hospital (New York, USA) data, and 72 hospital-acquired AKI and 303 NAKI COVID-19 patients from Tongji Hospital (Wuhan, China). Demographic, comorbidities, and longitudinal (3 days before and 3 days after AKI onset) clinical variables were used to compute odds ratios for and longitudinally predict hospital-acquired AKI onset.
Results
COVID-19 patients with AKI were more likely to die than NAKI patients (31.5% vs 6.9%, adjusted
p
< 0.001, OR = 4.67 95% CI 3.1, 7.0, Stony Brook data). AKI developed on average 3.3 days after hospitalization. Procalcitonin was elevated prior to AKI onset (
p
< 0.05), peaked, and remained elevated (
p
< 0.05). Alanine aminotransferase, aspartate transaminase, ferritin, and lactate dehydrogenase peaked the same time as creatinine, whereas
d
-dimer and brain natriuretic peptide peaked a day later. C-reactive protein, white blood cell and lymphocyte showed group differences − 2 days prior (
p
< 0.05). Top predictors were creatinine, procalcitonin, white blood cells, lactate dehydrogenase, and lymphocytes. They predicted AKI onset with areas under curves (AUCs) of 0.78, 0.66, and 0.56 at 0, − 1, and − 2 days prior, respectively. When tested on the Tongji Hospital data, the AUCs were 0.80, 0.79, and 0.77, respectively.
Conclusions
Time-locked longitudinal data provide insight into AKI progression. Commonly clinical variables reasonably predict AKI onset a few days prior. This work may lead to earlier recognition of AKI and treatment to improve clinical outcomes.
•Machine-learning model accurately predicts acute kidney injury onset in patients with COVID-19.•The top predictors were glomerular filtration rate, lactate dehydrogenase, alanine aminotransferase, ...aspartate aminotransferase, and C-reactive protein.•Longitudinal data yielded marked improvements in prediction accuracy over individual time points.•This approach could help identify patients for early interventions.
This study used the long-short-term memory (LSTM) artificial intelligence method to model multiple time points of clinical laboratory data, along with demographics and comorbidities, to predict hospital-acquired acute kidney injury (AKI) onset in patients with COVID-19.
Montefiore Health System data consisted of 1982 AKI and 2857 non-AKI (NAKI) hospitalized patients with COVID-19, and Stony Brook Hospital validation data consisted of 308 AKI and 721 NAKI hospitalized patients with COVID-19. Demographic, comorbidities, and longitudinal (3 days before AKI onset) laboratory tests were analyzed. LSTM was used to predict AKI with fivefold cross-validation (80%/20% for training/validation).
The top predictors of AKI onset were glomerular filtration rate, lactate dehydrogenase, alanine aminotransferase, aspartate aminotransferase, and C-reactive protein. Longitudinal data yielded marked improvement in prediction accuracy over individual time points. The inclusion of comorbidities and demographics further improves prediction accuracy. The best model yielded an area under the curve, accuracy, sensitivity, and specificity to be 0.965 ± 0.003, 89.57 ± 1.64%, 0.95 ± 0.03, and 0.84 ± 0.05, respectively, for the Montefiore validation dataset, and 0.86 ± 0.01, 83.66 ± 2.53%, 0.66 ± 0.10, 0.89 ± 0.03, respectively, for the Stony Brook Hospital validation dataset.
LSTM model of longitudinal clinical data accurately predicted AKI onset in patients with COVID-19. This approach could help heighten awareness of AKI complications and identify patients for early interventions to prevent long-term renal complications.
Routine feeding jejunostomy tube post esophagectomy is being revaluated because of its associated postoperative complications. We performed a systematic review and meta-analysis to evaluate the ...effect of routine feeding jejunostomy tube insertion on mortality and postesophagectomy outcomes.
Electronic databases (MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials) were queried through December 2020. Included studies compared esophagectomy with and without postoperative feeding jejunostomy. The primary outcome was 30-day mortality. Secondary outcomes included readmission rate, length of stay, postoperative complications (sepsis, pneumonia, chyle leakage, and anastomotic leakage), and duration of surgery. Random effects pairwise meta-analysis was used to compare groups, and the risk of bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool.
The meta-analyses of 12 studies (2 randomized controlled trials, 10 observational) that enrolled 36,284 participants showed lower 30-day all-cause mortality in the jejunostomy tube group (risk ratio RR = 1.53 95% CI, 1.37-1.70, P < .01; I2 = 0%, P = .80). Duration of surgery favored the no jejunostomy group (mean difference = −37.18; 95% CI, −59.48 to −14.87; P < .01). However, the 2 groups were not different in incidence of anastomotic leakage (RR = 0.88; 95% CI, 0.61-1.28; P = .50), length of stay (mean difference = −0.22; 95% CI, −1.34-0.89; P = .69), readmission (RR = 0.97; 95% CI, 0.92-1.02; P = .20), chyle leakage (RR = 1.05; 95% CI, 0.34-3.27; P = .94), sepsis (RR = 1.20; 95% CI, 0.96-1.50; P = .11), pneumonia (RR = 0.88; 95% CI, 0.75-1.03; P = .11).
Feeding jejunostomy tube after esophagectomy might lead to lower 30-day all-cause mortality with no difference in common postesophagectomy complications. A routine insertion of a jejunostomy tube should be considered at the time of surgery for esophageal cancer resection.
Overview of study methodology and results: J-tube insertion has a lower risk of 30-day mortality and should be considered for patients. Patients with J-tubes are not at increased risk of postoperative complications, increased LOS, or hospital readmission. Future research should evaluate specific low-risk subgroups that can benefit from direct oral feeding. Display omitted
ABSTRACT
Background
Although coronavirus disease 2019 (COVID-19) patients who develop in-hospital acute kidney injury (AKI) have worse short-term outcomes, their long-term outcomes have not been ...fully characterized. We investigated 90-day and 1-year outcomes after hospital AKI grouped by time to recovery from AKI.
Methods
This study consisted of 3296 COVID-19 patients with hospital AKI stratified by early recovery (<48 hours), delayed recovery (2–7 days) and prolonged recovery (>7–90 days). Demographics, comorbidities and laboratory values were obtained at admission and up to the 1-year follow-up. The incidence of major adverse cardiovascular events (MACE) and major adverse kidney events (MAKE), rehospitalization, recurrent AKI and new-onset chronic kidney disease (CKD) were obtained 90-days after COVID-19 discharge.
Results
The incidence of hospital AKI was 28.6%. Of the COVID-19 patients with AKI, 58.0% experienced early recovery, 14.8% delayed recovery and 27.1% prolonged recovery. Patients with a longer AKI recovery time had a higher prevalence of CKD (P < .05) and were more likely to need invasive mechanical ventilation (P < .001) and to die (P < .001). Many COVID-19 patients developed MAKE, recurrent AKI and new-onset CKD within 90 days, and these incidences were higher in the prolonged recovery group (P < .05). The incidence of MACE peaked 20–40 days postdischarge, whereas MAKE peaked 80–90 days postdischarge. Logistic regression models predicted 90-day MACE and MAKE with 82.4 ± 1.6% and 79.6 ± 2.3% accuracy, respectively.
Conclusion
COVID-19 survivors who developed hospital AKI are at high risk for adverse cardiovascular and kidney outcomes, especially those with longer AKI recovery times and those with a history of CKD. These patients may require long-term follow-up for cardiac and kidney complications.
Graphical Abstract
Graphical Abstract
Clinical evidence points to neuroprotective effects of smoking in Parkinson's disease (PD), but the molecular mechanisms remain unclear. We investigated the pharmacological pathways involved in these ...neuroprotective effects, which could provide novel ideas for developing targeted neuroprotective treatments for PD. We used the ETC complex I inhibitor methylpyridinium ion (MPP
) to induce cell death in SH-SY5Y cells as a cellular model for PD and found that nicotine inhibits cell death. Using choline as a nicotinic acetylcholine receptor (nAChR) agonist, we found that nAChR stimulation was sufficient to protect SH-SY5Y cells against cell death from MPP
. Blocking α7 nAChR with methyllycaconitine (MLA) prevented the protective effects of nicotine, demonstrating that these receptors are necessary for the neuroprotective effects of nicotine. The neuroprotective effect of nicotine involves other pathways relevant to PD. Cleaved Poly (ADP-ribose) polymerase-1 (PARP-1) and cleaved caspase-3 were decreased by nicotine in 6-hydroxydopamine (6-OHDA) lesioned mice and in MPP
-treated SH-SY5Y cells. In conclusion, our data indicate that nicotine likely exerts neuroprotective effects in PD through the α7 nAChR and downstream pathways including PARP-1 and caspase-3. This knowledge could be pursued in future research to develop neuroprotective treatments for PD.
This study investigated whether acute liver injury (ALI) persisted and identified predictors of ALI recovery as indicated by alanine aminotransferase (ALT) level at hospital discharge and 2 months ...post-discharge for 7595 hospitalized COVID-19 patients from the Montefiore Health System (03/11/2020-06/03/2021). Mild liver injury (mLI) was defined as ALT = 1.5-5 ULN, and severe livery injury (sLI) was ALT ≥ 5 ULN. Logistic regression was used to identify predictors of ALI onset and recovery. There were 4571 (60.2%), 2306 (30.4%), 718 (9.5%) patients with no liver injury (nLI), mLI and sLI, respectively. Males showed higher incidence of sLI and mLI (p < 0.05). Mortality odds ratio was 4.15 95% CI 3.41, 5.05, p < 0.001 for sLI and 1.69 95% CI 1.47, 1.96, p < 0.001 for mLI compared to nLI. The top predictors (ALT, lactate dehydrogenase, ferritin, lymphocytes) accurately predicted sLI onset up to three days prior. Only 33.5% of mLI and 17.1% of sLI patients (survivors) recovered completely at hospital discharge. Most ALI patients (76.7-82.4%) recovered completely ~ 2 months post-discharge. The top predictors accurately predicted recovery post discharge with 83.2 ± 2.2% accuracy. In conclusion, most COVID-19 patients with ALI recovered completely ~ 2 months post discharge. Early identification of patients at-risk of persistent ALI could help to prevent long-term liver complications.
Abstract
Background
Although SSRIs are no longer widely prescribed for post-stroke motor recovery, fluoxetine demonstrated beneficial effects on post-stroke depression (PSD). Given the potential side ...effects of SSRIs, targeted initiation among individuals at highest risk for PSD warrants consideration. While previous studies have identified stroke severity and psychiatric history as factors associated with PSD, its predictability remains unknown. In this study, we investigate inpatient predictive factors to better identify individuals who might derive the most benefit from targeted initiation of SSRIs.
Methods
We performed a retrospective analysis of a prospectively-collected registry of adult patients presenting with acute ischemic stroke to a tertiary referral urban academic comprehensive stroke center between 2016–2020. Patients were seen 4–6 weeks post-discharge and administered the PHQ-9 (Patient Health Questionnaire-9) to screen for PSD (PHQ-9 ≥ 5). Demographics, history of depression, stroke severity, and inpatient PHQ-9 scores were abstracted. Logistic regression was used to determine factors associated with PSD and an ROC analysis determined the predictability of PSD in the inpatient setting.
Results
Three hundred seven individuals were administered the PHQ-9 at follow-up (mean age 65.5 years, 52% female). History of depression (OR = 4.11, 95% CI: 1.65–10.26) and inpatient PHQ-9 score (OR = 1.17, 95% CI: 1.06–1.30) were significantly associated with PSD. Stroke severity, marital status, living alone, employment, and outpatient therapy were not associated with PSD. The ROC curve using a positive inpatient PHQ-9 achieved an area under the curve (AUC) of 0.65 (95% CI: 0.60–0.70), while the AUC was 0.72 (95% CI: 0.66–0.77) after adding history of depression.
Conclusions
History of depression and a positive inpatient PHQ-9 appear to be most strongly predictive of long-term PSD. Initiating SSRIs only in those individuals at highest risk for PSD may help reduce the burden of stroke recovery in this targeted population while minimizing adverse side effects.
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