COVID-19 in the Healthy Patient Population

In healthy patient population, COVID-19 remains importantly associated with unwholesomeness and mortality. While senesce remains the most crucial predictor of in-hospital outcomes, thromboinflammatory interactions are besides associated with worse clinical outcomes regardless of age in healthy patients. In a retrospective discipline design, consecutive patients without service line comorbidities hospitalized with confirm COVID-19 were included. Patients were subdivided into ≤55 and > 55 years of senesce. Predictors of in-hospital mortality or mechanical ventilation were analyzed in this patient population, a well as subgroups. stable parameters in overall and subgroup models were used to construct a bunch model for phenotyping of patients. Of 1207 COVID-19–positive patients, 157 met the study criteria ( 80≤55 and 77 > 55 years of age ). Most authentic predictors of outcomes overall and in subgroups were age, initial and follow-up d-dimer, and LDH ( lactate dehydrogenase ) levels. Their predictive cutoff values were used to construct a cluster model that produced 3 independent clusters. Cluster 1 was a low-risk bunch and was characterized by younger patients who had low thrombotic and incendiary features. Cluster 2 was intermediate risk that besides consisted of younger population that had tone down grade of thrombosis, higher inflammatory cells, and inflammatory markers. Cluster 3 was a bad bunch that had the most aggressive thrombotic and inflammatory feature. Coronavirus disease 2019 ( COVID-19 ) can infect patients in any old age group including those with no comorbid conditions. Understanding the demographic, clinical, and testing ground characteristics of these patients is authoritative toward developing successful discussion strategies.


  • Coronavirus disease 2019 ( COVID-19 ) adversely affects the older population and those with risk factors ; however, it can be fatal and is associated with meaning in-hospital deathrate or motivation for mechanical ventilation in healthier patients and in the young population .
  • d-dimer and LDH ( breastfeed dehydrogenase ) elevation and their re-elevation look to be linked to worse in-hospital outcomes careless of age in the goodly patient population .
  • Aging process, inflammation-induced thrombosis, endothelial infection, or multiple insistent consecutive diseased insults of unlike mechanisms can contribute to the elevation and re-elevation of d-dimer and LDH .
  • Unsupervised cluster model using age, d-dimer, and LDH initial and follow-up values produced 3 clusters in terms of in-hospital outcomes that could be identified as low, intermediate, and eminent risk. The 3 clusters were clear-cut in their thrombosis, inflammation, and end-organ wrong behavior .
  • Understanding of such diseased processes and their impact on patients with and without comorbidities among all age categories is crucial toward developing successful treatment strategies .

Coronavirus disease 2019 ( COVID-19 ) is caused by the recently identified coronavirus besides known as severe acute respiratory syndrome coronavirus 2 ( or SARS-CoV-2 ). 1 Since December 2019, COVID-19 has caused a global outbreak and is still spreading quickly in > 100 countries. 2 Our understanding of this disease has been increasing with time. A disease entity that was initially identified as a chiefly respiratory illness has slowly emerged as a systemic syndrome that causes endothelial dysfunction leading to microthrombosis and hard incendiary reply leading to a cytokine storm. 3 however, the pathophysiological and clinical characterization of the disease is still evolving. The disease still remains significantly heterogeneous both in its demographic characteristics and clinical features. One case of such heterogeneity is that this disease was thought to vastly affect the older population with significant comorbidities such as high blood pressure and diabetes mellitus. 4 however, in our clinical have, we have started to see significant unwholesomeness and deathrate in younger and otherwise healthy patient population. With the lack of clinical and pathophysiological studies in this subgroup of healthy patients with COVID-19, we sought to initiate a retrospective analyze in patients hospitalized with COVID-19 infection. The goal of this study was to characterize the clinical and testing ground findings and sympathize predictors of consequence in this patient population .


In a retrospective study plan, back-to-back adult patients admitted to our hospital between March 15 and April 23, 2020, with confirmed COVID-19 were reviewed. Patients were included if they had no baseline comorbidities. Patients were excluded if their historic period was < 18 years, torso mass index ≥30 kg/m2, or if they had ≥1 baseline comorbidities, defined as history of high blood pressure, diabetes mellitus, bronchial asthma, chronic clogging pneumonic disease, HIV contagion, chronic liver disease, hepatitis B or C viral infection, congestive heart failure, coronary thrombosis artery disease, chronic kidney disease, end-stage nephritic disease, smoke, malignancy, or any other chronic discipline. The datum that support the findings of this survey are available from the corresponding writer upon reasonable request .

Study Population, Patient Triage, and Comparisons

Patients with symptoms suggestive of COVID-19 infection were identified in the emergency department. After initial judgment, basic clinical data including time from symptom onset and admission lab investigations were obtained. In summation, all patients had pneumonic imagination ( chest of drawers radiogram or computed imaging of the chest of drawers ) done. Patients subsequently had a rhinal swab for COVID-19 RNA to confirm the infection with SARS-Cov-2. Electronic medical charts were reviewed for the presence of baseline comorbidities, initial and subsequent lab values, treatment and remedy modalities, need for mechanical ventilation, and in-hospital mortality. All lab data were obtained on the same day of entrance fee, while follow-up lab values were obtained for LDH ( breastfeed dehydrogenase ) on the second day of admission and for D-dimer if a change in the clinical condition ( change in the requirement of oxygen or full of life signs or clinical suspension of thromboembolic episode ). Patients were then classified based on their senesce as younger ( ≤55 years of age ) or older ( > 55 [ older ] years of senesce ). We selected a 55-year-old cutoff rate to differentiate between younger and older patients based in the US Census Bureau report, which defines older adults as ≥55 years of long time and aged as ≥65 years of historic period. 5 Subgroups were compared for their demographic, clinical, and testing ground variables and outcomes. The primary end point was in-hospital mortality and want for mechanical breathing .

Statistical Analysis

continuous variables were expressed as mean±SD, and nominal and categoric variables were expressed as numbers ( % ). The independent samples Student t examination and 1-way ANOVA were used to compare the mean values of different groups, and χ2 screen was used for comparison of nominal and categoric variables. Predictors of mortality and mechanical public discussion were checked using univariate and multivariate Cox regression models. The most stable predictors in all models were used to construct an SPSS unsupervised 2-step cluster exemplary to test the presence of the natural subgroups and were subsequently compared for characterization of each bunch of patients. Kaplan-Meier survival curves were used to test the deviation in accumulative in-hospital outcomes in different clusters. For all statistical tests, P < 0.05 was considered statistically significant. All analyses were performed with commercially available software ( SPSS, version 23.0 ; SPSS, Inc ) .


During the cogitation period, 1207 patients with confirm COVID-19 were identified. The baseline demographic, clinical, lab, and imaging criteria for all patients are summarized in Table I in the Data Supplement. Of these patients, 7 patients were excluded as their age was < 18 years, and 1050 patients were excluded due to the bearing of ≥1 comorbidities ( 734 [ 70 % ] with high blood pressure, 549 [ 52 % ] with diabetes mellitus, 306 [ 29 % ] patients were smokers, 79 [ 8 % ] with HIV infection, 154 [ 15 % ] with asthma, 119 [ 11 % ] with chronic clogging pneumonic disease, 7 [ 1 % ] with chronic liver-colored disease, 4 [ 0.4 % ] with hepatitis B infection, 50 [ 5 % ] with hepatitis C infection, 117 [ 11 % ] with congestive kernel bankruptcy, 125 [ 12 % ] with coronary thrombosis artery disease, 98 [ 9 % ] with chronic kidney disease, 82 [ 8 % ] with end-stage nephritic disease, and 12 [ 1 % ] patients with other causes ). One hundred fifty-seven patients met the analyze criteria and were included for final examination analysis. The bastardly age of the learn population was 52.6±17 years. Eighty ( 50.9 % ) patients were ≤55 years of historic period, and 46 ( 29 % ) were women. Of the study population, 105 ( 67 % ) patients received hydroxychloroquine/azithromycin combination, and 13 ( 8 % ) patients received tocilizumab. Thirty ( 19 % ) patients were mechanically ventilated, and 25 ( 16 % ) patients died during to the hospital path. Of bill, while all initial testing ground data were obtained at the day of admission, the second LDH values were obtained on the second day of entree and the second D-dimer was obtained within a week from the inaugural obtained D-dimer in 137 ( 87 % ) patients ( 44 [ 32 % ] on the second sidereal day, 43 [ 31 % ] on the third sidereal day, 21 [ 15 % ] on the one-fourth day, 15 [ 11 % ] on the one-fifth day, 7 [ 5 % ] on the sixth day, and 7 [ 5 % ] on the seventh day ), while the remaining patients had their D-dimer checked in the second week from the entrance fee ( overall mean difference between the first and second D-dimer was 3.9±7.8 days ). When patients were classified based on their long time, it was found that patients > 55 years of age had lower lymphocyte count, and lower albumin levels. Older patients were besides noted to have higher D-dimer, LDH ( breastfeed dehydrogenase ), breastfeed, ferritin, and creatinine levels ( Table 1 ). There were no differences between the two groups with respect to sex, body mass exponent, and lab values including high-sensitivity C-reactive protein, hemoglobin levels, white cell count, platelet count, prothrombin time, partial thromboplastin time, bilirubin, serum protein, and procalcitonin levels ( Table 1 ). The two groups were alike in terms of medicine use. Older affected role subgroup had higher mortality and higher need for mechanical ventilation ( board 1 ). In a subgroup psychoanalysis of patients who died, we noted that these patients were older and had higher D-dimer, LDH, breastfeed, and procalcitonin levels ( Table 1 ) .

Table 1. Demographic, Clinical, and Laboratory Data for All Patients and Subgroups
No Comorbidities (n=157) Age ≤55 y (n=80) Age >55 y (n=77) P Value Alive (n=132) Expired (n=25) P Value
Age, y 52.6±17 40.5±9.5 66.6±12.2 <0.001 50.6±17.1 62.4±12.7 0.001
Sex (male), n (%) 111 (71) 55 56 0.123 89 20 0.376
Body mass index, kg/m2 26.4±3.1 26.7±2.8 26±3.4 0.165 26.3±3.2 26.7±2.8 0.561
Onset of symptoms, d 6.5±4.6 6.9±4.6 6.1±4.6 0.686 6.9±4.8 5.1±3 0.064
Length of stay, d 7.7±5.6 7.34±5.4 8.2±6 0.359 7.4±5.5 9.3±6 0.111
Abnormal chest radiograph or computed tomography, n 132 69 63 0.686 108 24 0.315
 Hemoglobin, g/dL 13.9±1.8 14.1±1.5 13.8±2.13 0.374 13.9±1.8 14±2 0.720
 Platelets, 103/uL 254.3±130.9 219.3±100.4 217.8±97.3 0.926 218.4±96.2 219.3±111 0.966
 White cell count, 103/uL 8.4±3.9 8.1±3.8 8.7±4.1 0.418 8.14±3.7 9.4±4.8 0.126
 Neutrophils, 103/uL 6.8±3.8 6.49±3.8 7.2±3.9 0.273 6.5±3.7 8.1±4.5 0.064
 Lymphocytes, 103/uL 0.96±0.5 1.04±0.49 0.87±0.5 0.044 1±0.48 0.8±0.6 0.06
  vitamin d-dimer, ng/mL 3140±9411 701.2±937 5656±12 954 0.002 1682±4759 8705±17608 <0.001
 LDH, units/L 514±279 451±215 575.8±320 0.01 456±214 730±380 <0.001
 High-sensitivity C-reactive protein, mg/L 139.2±104.3 141.3±109 137.1±100 0.830 133.4±109.3 165±74.9 0.208
 Ferritin, mg/mL 1256±1229 1270±1234 1243±1235 0.036 1295±1318 1107±804 0.506
 Lactate, mmol/L 2.1±1.7 1.7±0.8 2.4±2.23 0.036 1.85±1.05 3±3.2 0.003
 Prothrombin time, s 13.4±1.8 13.3±2.1 13.6±1.55 0.297 13.3±1.8 14±1.9 0.110
 Partial thromboplastin time, s 32.5±5.3 32.9±6.2 32±4.2 0.321 32.6±5.5 32.2±4.8 0.726
 Creatinine, mg/dL 1.05±0.6 0.9±0.2 1.2±0.74 0.001 1.01±0.5 1.21±0.59 0.094
 Alanine transaminase, units/L 50.1±43.8 49.8±36.2 50.3±50.4 0.951 49.7±38.8 51.6±61 0.847
 Aspartate transaminase, units/L 64.5±55.5 57.4±38.9 71.5±67.7 0.144 60.6±42.7 80.3±90.6 0.105
 Alkaline phosphatase, units/L 89.2±72.8 81.6±40.4 96.6±94.2 0.236 91±79.6 81.6±31.7 0.555
 Total bilirubin, g/dL 0.7±1.2 0.54±0.35 0.79±1.59 0.216 0.68±1.28 0.65±0.33 0.904
 Combined bilirubin, g/dL 0.26±0.55 0.18±0.14 0.33±0.75 0.145 0.25±0.61 0.27±0.19 0.896
 Total protein, g/dL 6.93±0.71 7.01±0.75 6.9±0.66 0.196 6.9±0.75 7±0.53 0.625
 Serum albumin, g/dL 3.7±0.53 3.9±0.56 3.59±0.47 0.001 3.76±0.5 3.6±0.5 0.311
 Procalcitonin, ng/mL 1.1±4.69 0.4±0.43 1.99±6.9 0.120 0.39±0.45 3.3±9.2 0.014
  vitamin d-dimer, ng/mL 1631±1032 1631±5293 5982±13 398 0.02 1469±3705 11934±19062 <0.001
 LDH, units/L 490±337 394±240 563±387 0.005 396±204 780±500 <0.001
Δ vitamin d-dimers, ng/mL 479±5419 903±4830 54±5960 0.390 −303±3497 3229±9075 0.002
ΔLDH, units/L −48±261 −71±226 −27±291 0.356 −76±198 50±405 0.026
Hydroxychloroquine, n (%) 108 53 55 0.122 84 22 0.067
Tocilizumab, n (%) 12 9 3 0.174 9 2 0.416
Need for mechanical ventilation, n (%) 22 7 15 0.001 2 20 <0.001
 Time to mechanical ventilation since hospital admission, d 8.6±11 6.05±5.8 7.3±6.5 0.217 6.8±5.8 5.5±7 0.338
Mortality, n (%) 25 5 20 <0.001
Mortality or mechanical ventilation, n (%) 27 7 20 0.002

Predictors of In-Hospital Mortality

The overall predictors of composite of in-hospital mortality or need for mechanical ventilation in patients without comorbidities are summarized in table 2. Univariate Cox regression mannequin showed that the freelancer predictors of in-hospital deathrate were age, D-dimer, and LDH levels. Multivariate Cox regression models, however, showed that D-dimer and LDH levels were the lone predictors of in-hospital mortality, while long time lost statistical significance. In patients ≤55 years of age with no comorbidities ( younger healthier patients ), the predictors of in-hospital mortality in the univariate Cox arrested development exemplary were lower hemoglobin, higher neutrophil consider, D-dimer, LDH, and total bilirubin level. In the multivariate regression psychoanalysis, neutrophil reckon, D-dimer, and LDH levels were the only freelancer predictors of in-hospital deathrate ( table 2 ) .

Table 2. Cox Regression Models for Predictors of the Composite of Mechanical Ventilation or Death in Younger and Older Patients
All (n=157) Patients ≤55 y of Age (n=80) Patients >55 y of Age (n=77)
Univariate Cox Model Multivariate Cox Model Univariate Cox Model Multivariate Cox Model Univariate Cox Model Multivariate Cox Model
Odds Ratio P Value Odds Ratio P Value Odds Ratio P Value Odds Ratio P Value Odds Ratio P Value Odds Ratio P Value
Age, y 1.036 0.003 1.012 0.477 1.081 0.144 1.014 0.562
Sex, n (%) 0.837 0.473 2.018 0.076 0.182 0.2
Body mass index, kg/m2 1.034 0.59 1.019 0.891 1.052 0.528
Onset of symptoms, d 0.924 0.137 0.995 0.949 0.912 0.144
White cell count, 103/uL 1.059 0.197 1.168 0.021 1.009 0.892
Neutrophils, 103/uL 1.063 0.176 1.173 0.017 1.27 0.045 1.002 0.977
Lymphocytes, 103/uL 0.589 0.317 0.578 0.572 1.013 0.981
Hemoglobin, g/dL 1.008 0.946 0.58 0.032 0.287 0.092 1.231 0.077
Platelets, 103/uL 0.999 0.553 1.002 0.404 0.994 0.068
five hundred-dimer, ng/mL 1 <0.001 1 0.015 1.001 <0.001 1.001 0.008 1 0.004 1 0.026
LDH, units/L 1.002 <0.001 1.001 0.01 1.004 0.015 1.007 0.009 1.001 0.006 1.001 0.256
High-sensitivity C-reactive protein, mg/L 1.001 0.572 1.002 0.513 1.001 0.726
Ferritin, mg/mL 1 0.381 0.999 0.091 1 0.862
Prothrombin time, s 1.133 0.165 1.226 0.151 1.317 0.137
Partial thromboplastin time, s 0.993 0.859 0.892 0.259 1.171 0.027 1.13 0.054
Creatinine, mg/dL 1.556 0.085 0.593 0.774 1.267 0.44
Alanine transaminase, units/L 1.001 0.898 0.965 0.096 1.003 0.52
Aspartate transaminase, units/L 1.005 0.105 0.991 0.486 1.006 0.073
Alkaline phosphatase, units/L 0.999 0.734 1.001 0.956 0.999 0.738
Total bilirubin, g/dL 0.945 0.782 1.049 0.965 0.921 0.738
Combined bilirubin, g/dL 1.01 0.975 35.093 0.036 0.893 0.961 0.835 0.732
Total protein, g/dL 0.968 0.896 0.903 0.839 1.486 0.295
Serum albumin, g/dL 0.74 0.402 0.541 0.34 1.422 0.505
Procalcitonin, ng/mL 1.022 0.342 1.213 0.817 0.967 0.663
Hydroxychloroquine, n (%) 3.03 0.138 37.186 0.323 1.392 0.665
Tocilizumab, n (%) 0.351 0.319 0.97 0.978 0.046 0.615

In older patients ’ subgroup, higher D-dimers, LDH, and fond thromboplastin time levels were the predictors of in-hospital mortality by univariate Cox regression model. Multivariate arrested development analysis, however, showed that only higher D-dimer predicted in-hospital deathrate ( table 2 ) .

Follow-Up five hundred-Dimer and LDH

Most consistent predictors of the complex outcomes careless of age in all models were found to be D-dimer and LDH. Follow-up values of D-dimer and LDH were found to be significantly higher in patients > 55 years of senesce and in patients who died, compared with patients ≤55 years of age and those who survived ( P =0.02 and 0.005, respectively ; Table 1 ). We besides analyzed ΔD-dimer and ΔLDH and noted that they were not importantly unlike between younger and older patient subgroups ( P =0.390 and 0.356, respectively ). however, these values were importantly higher in patients who died compared with patients who survived ( P =0.002 and 0.026, respectively ; Table 1 ). telephone receiver operating characteristic was done for initial and follow-up ( second ) D-dimer and LDH, a well as their delta values ( Figure 1 ), and we found that the best predictors for composite outcomes for initial, follow-up, and ΔD-dimer were when their values were > 461, 491, and 798 ng/mL, respectively. similarly, the best predictors for initial, follow-up, and ΔLDH were when their values were > 467, 505, and 128 units/L, respectively .Figure 1. Figure 1. Receiver operating characteristics curve for best predictors of in-hospital outcomes. AUC indicates area under the swerve ; and LDH, lactate dehydrogenase . Based on these cutoff values, Cox arrested development models were repeated with adaptation for both old age and arouse for all patients ( table 3 ), and it was noted that all adjusted variables can significantly predict outcomes based on univariate models, whereas in multivariate models, merely adjusted ΔD-dimer > 798 ng/mL and duplicate LDH level of > 505 units/L predicted deathrate .

Table 3. Cox Regression for Prediction of the Composite of Need of Mechanical Ventilation or Death Adjusted for Age and Sex in All Patients
Univariate Model Multivariate Model
Odds Ratio P Value Odds Ratio P Value
Initial five hundred-dimer, >461 ng/mL 3.99 0.008 2.5 0.156
Initial LDH, >467 unit/L 3.1 0.028 0.86 0.816
Second d-dimer, >491 ng/mL 11.4 0.001 3 0.248
Second LDH, >505 unit/L 6 <0.001 4.7 0.023
Δ vitamin d-dimer, >798 ng/mL 4.59 0.001 3.1 0.034
ΔLDH, >128 unit/L 3 0.049 0.77 0.686

Cluster Model Prediction of Composite Outcome Based on Age, d-Dimer, and LDH

adjacent, a 2-step bunch model was initiated for all patients based on long time, initial d-dimer > 461 ng/mL, follow-up d-dimer > 491 ng/mL, initial LDH > 467 unit/L, and follow-up LDH > 505 unit/L ( Figure 2 ). The output signal revealed that patients were classified into 3 different clusters. Based on Kaplan-Meier wind for survival free of the composite outcomes, it was noted that bunch 1 was a low-risk bunch, bunch 2 was an intermediate-risk bunch, and bunch 3 was a bad cluster ( deathrate and mechanical public discussion : 0 [ 0 % ], 16 [ 27 % ], and 11 [ 55 % ], respectively ; P < 0.001 ; Table 4 ). Post hoc examination of the clusters revealed the follow characteristics for each bunch :

Table 4. Comparison Between Different Clusters for Demographic, Clinical, and Laboratory Variables, As Well As Treatment Options and Outcomes
Cluster 1 (Low Risk, n=33) Cluster 2 (Intermediate Risk, n=60) Cluster 3 (High Risk, n=20) P Value
Age, y 51.4±14.7 54.3±15.9 64.8±8.7 0.005*†
Sex (male), n (%) 23 49 16 0.400
Body mass index, kg/m2 26.7±3.3 26.5±3 26.6±2.5 0.968
Onset of symptoms, d 6.3±4.3 6.5±4 7.1±4.1 0.787
Length of stay, d 6.2±3.9 9.3±7.1 7.7±4.2 0.05‡
 Hemoglobin, g/dL 13.4±1.3 14.1±1.8 14.3±2.5 0.137
 Platelets, 103/uL 186±89.9 236.2±114.6 223.3±78.5 0.08
 White cell count, 103/uL 6.9±3.6 8.7±3.7 10.7±4.4 0.003*
 Neutrophils, 103/uL 5.4±3.6 7.4±3.6 9.1±4.1 0.002*‡
 Lymphocytes, 103/uL 1±0.43 0.8±0.37 0.92±0.68 0.254
  five hundred-dimer, ng/mL 260±126 2133±5053.3 13 332±19 692 <0.001*†
 LDH, units/L 315±87 545±187 822±395 <0.001*†‡
 High-sensitivity C-reactive protein, mg/L 104.7±105 166±101 158±80 0.024‡
 Ferritin, mg/mL 756±669 1408±1097 1880±1938 0.006*‡
 Lactate, mmol/L 1.5±0.6 1.95±0.9 3.7±3.5 <0.001*†
 Prothrombin time, s 13.2±1.2 13.4±1.7 14.6±2.1 0.015†
 Partial thromboplastin time, s 32±3.7 32.9±6.6 32.4±5 0.784
 Creatinine, mg/dL 1±0.2 1.01±0.48 1.54±0.96 <0.001*†
 Alanine transaminase, units/L 43.8±33.8 51.5±40.1 65.5±66.3 0.245
 Aspartate transaminase, units/L 49±30 66.9±46.5 94.7±93.5 0.018*†
 Alkaline phosphatase, units/L 74.9±30 82.8±43.3 84.9±37.4 0.584
 Total bilirubin, g/dL 0.5±0.41 0.6±0.3 0.78±0.42 0.036*
 Combined bilirubin, g/dL 0.17±0.1 0.2±0.1 0.34±0.25 0.002*†
 Total protein, g/dL 6.8±0.9 6.9±0.57 7±0.6 0.645
 Serum albumin, g/dL 3.8±0.5 3.7±0.46 3.5±0.52 0.099
 Procalcitonin, ng/mL 0.34±0.41 1.8±6.4 0.5±0.56 0.496
  five hundred-dimer, ng/mL 290.8±78.7 459.6±305 874.5±407 <0.001*‡†
 LDH, units/L 288±112 2355±54 248 15 443±20 454 <0.001*†
Hydroxychloroquine, n (%) 25 55 15 0.063
Tocilizumab, n (%) 0 8 1 0.065
Need for mechanical ventilation, n (%) 0 11 11 <0.001
Mortality, n (%) 0 14 11 <0.001
Mortality or mechanical ventilation, n (%) 0 16 11 <0.001


Clusters 1 and 2 were significantly younger than cluster 3 ( 51.4±14.7, 54.3±15.9, and 64.8±8.7 years of long time, respectively ; P =0.005 ; Table 4 ) .

Thrombosis Burden

Thrombosis burden based on d-dimer level was found to increasingly increase from bunch 1 to clusters 2 and 3 ( 260±126, 2133±5053.3, and 13332±19692 ng/mL, respectively ; P < 0.001 ; Table 4 ) .

Blood Cells

While hemoglobin and platelet levels were not different between clusters, white cell count was the lowest in bunch 1 compared with the other two clusters ( 6.9±3.6, 8.7±3.7, and 10.7±4.4 10/3mL, respectively ; P =0.002 ; Table 4 ). Deferential white count showed that this difference was chiefly due to neutrophil count, which increasingly increased from bunch 1 to cluster 3 ( 5.4±3.6, 7.4±3.6, and 9.1±4.1 103/mL, respectively ; P =0.002 ; Table 4 ), while lymphocyte count was not different between clusters .

Inflammatory Markers

It was noted that for acute-phase reactants such as high-sensitivity C-reactive protein and ferritin significantly and increasingly increased from bunch 1 to cluster 3 ( high-sensitivity C-reactive protein : 104.7±105, 166±101, and 158±80 mg/d, respectively, P =0.024 ; ferritin : 756±669, 1408±1097, and 1880±1938 mg/mL, respectively, P =0.006 ; Table 4 ) .

Target Organ Damage and Tissue Damage

Parameters suggestive of aim organ damage such as nephritic officiate tests and liver function tests and parameters of tissue hypoperfusion such as breastfeed levels were checked for all clusters. Target organ damage and tissue hypoperfusion were the hallmark of cluster 3 as suggested by difference in creatinine ( cluster 1, 1±0.2 mg/dL ; bunch 2, 1.01±0.48 mg/dL ; cluster 3, 1.54±0.96 mg/dL ; P < 0.001 ), aspartate transaminase ( cluster 1, 49±30 units/L ; bunch 2, 66.9±46.5 units/L ; cluster 3, 94.7±93.5 units/L ; P =0.018 ), total bilirubin ( cluster 1, 0.5±0.41 mg/dL ; bunch 2, 0.6±0.3 mg/dL ; bunch 3, 0.78±0.42 mg/dL ; P =0.036 ), direct bilirubin ( bunch 1, 0.17±0.1 mg/dL ; bunch 2, 0.2±0.1 mg/dL ; cluster 3, 0.34±0.25 mg/dL ; P =0.002 ), and lactate levels ( bunch 1, 1.5±0.6 ng/mL ; bunch 2, 1.95±3.7 ng/mL ; cluster 3,3.7±3.5 ng/mL ; P < 0.001 ; Table 4 ). In addition, weave wrong as suggested by LDH levels increasingly increased from cluster 1 to clusters 2 and 3 ( bunch 1, 315±87 unit/L ; bunch 2, 545±187 unit/L ; bunch 3, 822±395 unit/L ; P < 0.001 ; Table 4 ) .


To the best of our cognition, this is the first report describing the demographic, clinical, and lab parameters of healthy patient population hospitalized with COVID-19. The chief findings of the current study can be summarized as follows : the independent predictors of in-hospital outcomes ( mortality or indigence for mechanical public discussion ) are older age and high D-dimer and LDH levels. D-dimer and LDH seemed to be the most consistent predictors of adverse outcomes careless of age. furthermore, the predictive ability of both variables increased when these were followed up during hospitalization with the best sensitivity acquired by second high D-dimer measurement and the best specificity observed for significant incontrovertible change of D-dimer ( ΔD-dimer ) on subsequent test. last, when patients were classified with an unsupervised cluster using their historic period, initial, and follow-up D-dimer, and LDH, the model output created 3 different phenotypes with distinct in-hospital outcomes. importantly, two of these clusters were relatively younger patients who were differentiated based on their D-dimer, inflammatory markers, and parameters of end-organ damage, and the third bunch consisted of older patients with the significantly worst clinical and testing ground profile. Despite the great feel gained in the clinical characterization and diagnosis, COVID-19 remains a active disease process for which our understanding continues to evolve from what was initially identified as a primarily respiratory illness toward a systemic syndrome that involves multiorgan endothelial dysfunction powerfully linked to microthrombosis and severe incendiary response. The heterogeneity of the disease is demonstrated by the fact that it continues to significantly affect all age groups and not just the older population or those with comorbidities, particularly diabetes mellitus and high blood pressure. 6 recent studies showed that children and young adults who died had besides had baseline comorbidities and that those who did not have baseline comorbidities could be managed on general wards and were discharged successfully. 7, 8

Demographic, Laboratory, and Clinical Characteristics of the Disease

In our report, of 1207 patients admitted with COVID-19 during the earlier period of the pandemic, 157 of them ( 13 % ) had no baseline comorbidities, which was slightly more than that reported by the Centers for Disease Control ( 7.9 % ). 9 twenty-seven ( 17 % ) of all patients reported here had in-hospital outcomes. importantly, 80 of these patients were < 55 years of age, of whom 7 ( 9 % ) had in-hospital outcomes, suggesting that rates of serious adverse outcomes are not a depleted as initially expected at the begin of the pandemic. While age remained an important predictor of deathrate, sexual activity failed to predict mortality overall and in subgroups. When patients were classified based on their age, it was found that older patients ( > 55 years of old age ) had lower lymphocyte consider, serum albumin, and platelet count and higher LDH, creatinine, and lactic acidic, and the most remarkable remainder between both groups was the significantly elevated D-dimer levels in the older group. This was confirmed in the Cox arrested development models when only D-dimer and LDH were the stable predictors of deathrate regardless of the age. This suggests that the primary coil pathophysiology in this group of patients is thrombotic in nature while all early changes can be secondary. The fact that the follow-up D-dimer and ΔD-dimer levels in hospital were the parameters that could predict deathrate with high sensitivity and specificity, respectively, suggests that this thrombotic burden is active and continues to rebound during the course of the disease. When unsupervised cluster model was applied to all patients, an significant connection between the incendiary status and thrombosis was uncovered pointing toward age-related progressive worsen of incendiary express coupled with the inflammatory cells and the inflammatory markers that seems linked to higher deathrate. It is important to note that such natural categorization of patients revealed that younger patients may not be exchangeable in their display in the disease and only those who have higher thromboinflammatory load are at an increased risk for in-hospital outcomes, suggesting that there may be underlying physiological or genetic factors that enhance such process in some individuals versus the others. On the other hand, the bunch exemplary suggested that in older age, this thromboinflammatory relationship seems to be the most aggressive, leading to higher in-hospital outcomes .

Proposed Pathophysiological Mechanisms

SARS-Cov-2 entry into the cells is mediated by ACE-2 ( angiotensin-converting enzyme 2 ) receptor on cellular membranes, 10 abolishing ACE-2 enzyme activity. 11 The expression of ACE-2 receptors may decrease with advanced age. 12 This may lead to a decrease metabolism of angiotensin II and a systemic imbalance between angiotensin II ( proinflammatory/procoagulant ) and angiotensin 1-7 ( anti-inflammatory/anticoagulant ), causing a worse baseline proinflammatory/procoagulant state in older patients that is aggravated by COVID-19 infection, which induces further downregulation of ACE-2 in older patients ( Figure 3 ) .Figure 2. Figure 2. Unsupervised computational cluster model. The exemplary was initiated after feature origin using best predictors of in-hospital outcomes from Cox regression mannequin and recipient hustler characteristic curl, namely historic period, initial and follow-up d-dimer above 461 and 491 ng/mL, respectively, and initial and follow-up lactate dehydrogenase 467 505 units/L, respectively. The mannequin end product showed 3 clusters. Based on Kaplan-Meier curl ( upper left ), cluster 1 was a low-risk cluster with zero outcomes ( black line ), cluster 2 was intercede gamble ( blue course ), and bunch 3 was high risk ( loss course ). Post hoc analysis for bunch description based on domains of historic period, thrombosis, incendiary cells, inflammatory markers, and target electric organ damage showed a increasingly worsening profile from clusters 1 to 3 .Figure 3. Figure 3. Proposed mechanisms of thromboinflammatory response in patients with no risk factor. ACE-2 indicates angiotensin-converting enzyme 2 ; AT-2, angiotensin-2 ; and SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2 . The aging procedure can besides impact the immune organization leading to a work of a less-competent immune organization referred to as immune agedness, 13 which incorporates dysfunction in cellular sign that immediately affects the ability to contain infections and in case of COVID-19 can lead to dysfunctional ( decreased or overexerted ) response to viral load and inability to control the viral spread. Another possible explanation is inflammation-induced thrombosis ( IIT ) —a phenomenon reported in several inflammatory and autoimmune diseases. 14 The pathogenesis of IIT is complicated and involves bidirectional interactions between the incendiary state and the curdling system ( Figure 3 ). Reports have shown that IIT can be initiated either on the proinflammatory goal or the procoagulant end. An lap of both ends of IIT may occur, causing a evil cycle that leads to rebound inflammation and thrombosis with a baseline increased proinflammatory submit as explained previously. This may explain why in our study the predictive ability of D-dimer elevation may shed sparkle on that a re-elevation of D-dimer may signify multiple episodes of thrombosis that is linked to high deathrate in those patients. importantly, such IIT is besides reported to develop in the absence of vascular endothelial damage, which may explain the pathology in younger patients observed in our learn. recent diseased reports have shown that viral elements exist within endothelial cells of patients with COVID-19 associated with accumulation of inflammatory cells, causing endotheliitis. 15 endothelial dysfunction occurring in COVID-19 can shift the vascular equilibrium toward more vasoconstriction, subsequent organ hypoperfusion, and ignition with associated procoagulant state of matter. This can explain the exalted breastfeed, LDH, and incendiary markers that seem to be related to the badness of the disease in our study. In patients with COVID-19, endotheliitis can lead to activation of the curdling shower to form fibrin meshes in an attempt to contain the viral banquet by forming microthrombi in situ, 16 which can potentially dislodge systemically leading to arterial and venous thrombosis. As such, the integrity of the endothelial function and immune system in the younger people is crucial for a regulated immune response good enough to control the virus circulate by forming microthrombi for viral entrapment, and this in turn may partially lead to a proportional quiescent systemic inflammatory state in the younger fitter patients. On the early hand, direct endothelial infection can besides aggravate the service line endothelial dysfunction that can exist because of comorbidities or due to aging, 17 which may partially explain the high levels of D-dimers and incendiary reply in the older patients in our discipline. An alternate explanation for the change in D-dimer with prison term can be the multiple injuries, or multiple reach to the endothelium by different consecutive mechanisms while the disease progresses. The consecutive hits can be composed of endothelial infection with the consociate thrombo-entrapment of the virus, incendiary reaction to the virus mediated by IIT, and hypoxia- or hypotension-induced thrombosis. 18

Phenotypes of the Disease in Patients Without Comorbidities

In our study, we have attempted to uncover the natural distribution of parameters between patients to understand phenotyping, and for that, we have used a machine learning–based unsupervised cluster model. computational cluster is an exploratory statistical method designed to uncover natural groups within data that would otherwise be invisible using traditional classification methods. Unsupervised bunch, particularly, separates patients into groups without a priori categorization. In our learn, unsupervised 2-step bunch psychoanalysis was used. In the first step, small preclusters were created, which are then merged according to the greatest switch in the outdistance quantify in the second gear step into the most meaningful clusters. The parameters used for clustering in our study were those that had the best prediction of mortality in terms of Cox regression models and receiver operate characteristic curves, namely age, initial and follow-up D-dimer, and LDH levels. The exemplary spit out 3 discrete clusters in terms of in-hospital outcomes that could be identified as low, intermediate, and high risk. The most matter to find was that the low- and intermediate-risk clusters were identical in terms of long time ( both largely represent younger patients ) and early demographic variables ; however, the low-risk cluster had no in-hospital outcomes and was characterized by the better visibility across all predefined disease domains, namely thrombosis, inflammation, inflammatory cells, and end-organ price. On the other hired hand, the intermediate-risk bunch showed an average floor of thrombosis, elevation of incendiary cells, and target organ damage and a higher level of incendiary markers. The bad bunch had the worst profile across all domains and had older long time. such clusters seem sanely explained based on the previously proposed mechanism. It seemed that the presumed more preserve endothelial serve in the intermediate-risk group leads to a less thrombosis burden compared with the bad group in which the mechanism of thromboinflammation were the most aggressive probably because of a worse country of endothelial dysfunction created by more pronounce aging process as explained previously .

Study Limitations

We acknowledge the comply limitations : first, the sample size is small, which can affect the results of all models. however, the incidence and preponderance of COVID-19 in patients with no comorbidities remain humble, making it difficult to gather information from an appropriate count of patients. As such, the findings of our survey should be confirmed by far larger multicenter studies. Second, the data and variables collected for our patients depended on the testing ground analyses that were done in the initial stages of the disease. other relevant lab findings that can confirm our guess such as troponin levels, and longer trends of D-dimers and LDH, adenine well as echocardiography, computed imaging thorax with contrast, or early imaging modalities, were not available for the huge majority of the patients. furthermore, the clock between the first and second D-dimer evaluation was based on clinical suspicion alone, as our study was not powered to detect the effect of prison term to change of D-dimer clinical outcomes ; further studies need to address this restriction. far studies should focus on recruiting patients after measuring these variables to get more insight into the pathophysiological mechanism. Third, most of the make bold mechanisms in the current learn are just assumptions, and despite that pathological reports exist, they are still rarely done, and to confirm the assume mechanisms, more detailed diseased reports are needed. Fourth, the 2-step cluster model that was used in our study functions good in the presence of large scale data, and as such, results from this model should be treated with concern. traditionally, sample size survival is based on the number of variables fed into the bunch model. The best set about to assess the appropriateness of the sample size is to determine whether the dimensionality of the data is not besides high for the number of cases to be grouped. The minimal sample size to include should be no < 2k cases ( k=number of variables ), preferably 5×2k. 19, 20 In our analysis, the number of variables fed into the model was 5, and consequently, the appropriate sample size for a relatively stable exemplar would be anywhere between 32 and 160. frankincense, our sample size of 157 patients would be sufficient. however, validation of all the models described in this report needs to be done on larger scale studies before any of these findings can be adopted into clinical practice. Fifth, the differences between the testing ground values in the conventional analysis ( table 1 ), despite statistically significant, were only elusive, and the think of values of parameters such as those of neutrophils, lymphocytes, creatinine, and liver serve tests appear to be normal. however, considerable count of patients had abnormal values of these parameters when flatly classified ( table II in the Data Supplement ). It is to be noted that when patients were classified based on the unsupervised cluster model using D-dimer, LDH, and senesce, the differences between these variables changed, demonstrating how a bunch analysis without a priori assumptions can change our expression into lab results based on the natural distribution of the classify attributes, compared with conventional methods of comparison, which depends on artificial separation of data based on arbitrary cutoff values such as historic period of 55 years used in the current study. last, while D-dimer elevates as the leave of thrombolysis, which represents an indirect evidence of thrombosis and thus used clinically to exclude thromboembolic episodes, D-dimer can besides be elevated in a diverseness of other situations including, pregnancy, excitement, malignity, injury, postsurgical treatment, liver-colored disease ( decrease clearance ), and heart disease. 21–23 It is besides frequently high in hospitalize patients. Given that the population studied is healthy at baseline excludes most of the nonthrombotic causes of D-dimer elevation and, furthermore, the high levels for patients on presentation and follow-up during their hospital bide, is reasonably not familiar to those noted for hospitalize patients .


In patients without service line comorbidities, COVID-19 can be fatal and is associated with meaning in-hospital outcomes defined as in-hospital mortality or need for mechanical ventilation. While age remains the most important forecaster, D-dimer and LDH natural elevation at baseline and follow-up during the hospital quell seems to be linked to worse in-hospital outcomes careless of historic period in these patients. The fundamental mechanisms seem to be related to an aging process, IIT, endothelial infection, or multiple hit from different consecutive diseased insults. Understanding of such diseased processes and their impingement on patients with and without comorbidities among all old age categories is all-important toward developing successful discussion strategies .

Nonstandard Abbreviations and Acronyms

ACE-2 angiotensin-converting enzyme 2
COVID-19 coronavirus disease 2019
IIT inflammation-induced thrombosis
LDH breastfeed dehydrogenase


none .


*These authors contributed equally to this article. The Data Supplement is available with this article at hypertext transfer protocol : // For Disclosures, see page 2774.

symmetry to : Sridhar Chilimuri, MD, Internal Medicine, BronxCare Hospital Center, 1650 Grand Concourse, Bronx, NY 10457. Email

[email protected]


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